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UNIVERSITY OF GHANA 
 
DEPARTMENT OF GEOGRAPHY AND RESOURCE DEVELOPMENT 
 
 
 
 
  
 
SPATIAL ANALYSIS OF MALARIA INCIDENCE 
AND INTERVENTIONS IN THE BRONG AHAFO 
REGION 
 
BY 
ASAMOAH-PEPRAH EUGENE 
(10552222) 
 
THIS THESIS IS SUBMITTED TO THE SCHOOL OF GRADUATE 
STUDIES, UNIVERSITY OF GHANA,LEGON, IN PARTIAL FULFILLMENT 
OF THE REQUIREMENTSFOR THE AWARD OF AN MASTER OF 
PHILOSOPHY DEGREE IN GEOGRAPHY AND RESOURCED 
DEVELOPMENT 
 
 
 
 
 
JULY, 2017 
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DECLARATION 
 
I, Asamoah- Peprah, Eugene, do hereby declare that this dissertation, titled "Spatial Analysis 
of Malaria Incidence and Interventions in the Brong Ahafo Region, is my own original 
research submitted to the School of Graduate Studies, University of Ghana for the degree of 
Masters of Philosophy in Geography and Resource Development, and that it has not been 
submitted neither in part nor in full for another degree in this or in any other university. 
 
 ………………………….                              Date ……………………………….                                                                                                                             
ASAMOAH-PEPRAH EUGENE                                                                        
                (Student)  
  
…………………………..                              Date ……………………………….  
DR ALEX BARIMAH OWUSU                                                       
       (Principal Supervisor) 
  
             ………………………….                               Date ………………………………  
DR EBENEZER NIKOI                                            
     (Co-Supervisor)                                                      
 
 
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DEDICATION 
 
This piece of work is dedicated to my parents and siblings who have contributed immensely 
to my education. 
 
    
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ACKNOWLEDGEMENT 
 
My ultimate thanks and acknowledgement goes to the Almighty God for his guidance, 
wisdom and protection throughout my work. I also offer my profound gratitude to my helpful 
and kind lecturers and supervisors, Dr Alex Barimah Owusu and Dr. Ebenezer Nikoi of the 
Department of Geography and Resource Development, University of Ghana, whom 
regardless of their busy schedules, have guided me throughout the work. I say thank you and 
may God richly bless you. 
Besides, I would be ungrateful if I did not render my sincere thanks to Dr Kenneth Peprah, a 
lecturer at the University for Development Studies (UDS) and Bishop Edwin Ewuise 
Brookman, of Grace Gospel Ministries International for their kind assistance in many ways. 
Finally, my grateful acknowledgement is expressed to the National Malaria Control 
Programme for giving me all the necessary information for the completion of this research 
and also to all the respondents in the Brong Ahafo Region who spared, their precious time to 
answer all questionnaires used for this study. I say thank you all. 
 
 
 
 
                                                         
 
 
 
 
 
 
 
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TABLE OF CONTENTS 
 
DECLARATION ............................................................................................................................ ii 
DEDICATION ............................................................................................................................... iii 
ACKNOWLEDGEMENT ............................................................................................................. iv 
TABLE OF CONTENTS ................................................................................................................ v 
LIST OF FIGURES ....................................................................................................................... ix 
ABBREVIATIONS ........................................................................................................................ x 
ABSTRACT .................................................................................................................................. xii 
CHAPTER ONE: INTRODUCTION ............................................................................................. 1 
1.1 Background ............................................................................................................................... 1 
1.2 Problem Statement .................................................................................................................... 6 
1.3 Research Questions ................................................................................................................... 9 
1.4 Aim and Objectives................................................................................................................... 9 
1.5 Research Hypotheses ................................................................................................................ 9 
1.6 Rationale of the Study ............................................................................................................. 10 
1.7 The Scope of the Study ........................................................................................................... 10 
1.8 Organization of Thesis ............................................................................................................ 10 
CHAPTER TWO: LITERATURE REVIEW ............................................................................... 12 
2.1 Introduction ............................................................................................................................. 12 
2.2 Global Malaria Distribution .................................................................................................... 12 
2.3 Malaria Situation in Africa ..................................................................................................... 14 
2.4 Malaria Situation in Ghana ..................................................................................................... 15 
2.5 Conditions Necessary for the Anopheles Mosquitoes Development ...................................... 16 
2.5.1 Temperature ......................................................................................................................... 17 
2.5.3 Relative humidity (RH)........................................................................................................ 20 
2.6 Anthropogenic Factors Affecting Malaria Transmission ....................................................... 21 
2.7 The use of GIS Technology in Malaria Study ........................................................................ 22 
2.8 The limitations in using GIS for Malaria Research Studies ................................................... 23 
2.9 Malaria Control Interventions ................................................................................................. 24 
2.10 Summary of literature review ............................................................................................... 26 
2.11 Conceptual Framework ......................................................................................................... 26 
CHAPTER THREE:  STUDY AREA AND METHODOLOGY ................................................ 32 
3.1 Introduction ............................................................................................................................. 32 
3.2 Study Area .............................................................................................................................. 32 
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3.3 Research Design...................................................................................................................... 35 
3.4 Study Population ..................................................................................................................... 36 
3.5 Sample Size ............................................................................................................................. 37 
3.6 Sampling Procedure ................................................................................................................ 40 
3.7 Research Instruments ............................................................................................................................ 40 
3.7.1 Questionnaire Survey ......................................................................................................................... 40 
3.7.2 In-Depth Interviews ........................................................................................................................... 41 
3.8 Data Collection Procedure .................................................................................................................... 41 
3.8.1 Map data............................................................................................................................................. 41 
3.8.2 Secondary Sources of Data ................................................................................................................ 42 
3.9 Methods of Data Analysis ..................................................................................................................... 42 
3.9.1 GIS software ...................................................................................................................................... 42 
3.9.2 Data analysis overview ...................................................................................................................... 42 
3.9.3 Getis and Ord Gi* statistic ................................................................................................................. 43 
3.9.4 Ordinary Least Square ....................................................................................................................... 45 
3.9.5 Geographically Weighted Regression ................................................................................................ 47 
CHAPTER FOUR: EXAMINING THE DISTRIBUTION OF MALARIA INCIDENCE ........................ 51 
4.1 Introduction ........................................................................................................................................... 51 
4.2 Perception of malaria incidence ............................................................................................................ 51 
4.3 Spatial patterns of malaria incidence .................................................................................................... 52 
4.3 Analysis of Hotspot Distribution of Malaria Incidence ........................................................................ 53 
4.4   Result of Major Factors Influencing the Pattern of Distribution ........................................................ 60 
CHAPTER FIVE: CORRELATION BETWEEN MALARIA INCIDENCE ............................................ 64 
AND INTERVENTIONS ........................................................................................................................... 64 
5.1 Introduction ........................................................................................................................................... 64 
5.2 Malaria Control Interventions ............................................................................................................... 64 
5.3 Malaria Control Interventions-GWR Results........................................................................................ 66 
CHAPTER SIX: MALARIA INTERVENTIONS EXISTING IN THE STUDY REGION AND 
THEIR EFFECTIVENESS. ........................................................................................................................ 73 
6.1 Introduction ........................................................................................................................................... 73 
6.2 Malaria Control Interventions in the Study Region .............................................................................. 73 
6.3 Indoor Residual Spraying (IRS) ............................................................................................................ 73 
6.3.1 Effectiveness of Indoor Residual Spraying (IRS) as an Intervention in the Study Region ................ 74 
6.4 Long Lasting Insecticide Nets (LLINs) ................................................................................................ 75 
6.4.1 Possession of LLINs as a Malaria Control Intervention .................................................................... 76 
6.4.2 Utilization of Long-Lasting Insecticide Net (LLINs) ........................................................................ 76 
6.5 Antimalarial drugs as malaria control intervention ............................................................................... 77 
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6.5.1 Effectiveness of Antimalarial Drugs as an Implemented Intervention .............................................. 78 
6.6 Herbal medicine as malaria control intervention .................................................................................. 79 
6.6.1 Effectiveness of Herbal Medicine as Intervention Implemented in the Study Area .......................... 80 
6.7 Interventions Targeting Malaria Incidence ........................................................................................... 81 
6.8 Challenges of the Interventions for Malaria Control ............................................................................ 82 
6.9 Coping Strategies to Manage Malaria ................................................................................................... 83 
CHAPTER SEVEN: SUMMARY, CONCLUSION AND RECOMMENDATIONS ............................... 84 
7.1 Introduction ........................................................................................................................................... 84 
7.2 Summary of the Major Findings ........................................................................................................... 84 
7.3 Conclusion ............................................................................................................................................ 88 
 7.4 Recommendations ................................................................................................................................ 90 
REFERENCES ........................................................................................................................................... 91 
APPENDIX I: QUESTIONNAIRE .......................................................................................................... 116 
APPENDIX II ........................................................................................................................................... 124 
 
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LIST OF TABLES 
 
TABLES                      PAGES 
Table 3.1 showing 27 administrative districts with their population ....................................... 36 
Table 3.2 Sample Size by Clusters .......................................................................................... 39 
Table 4. 1 Perceptions of Malaria Incidence in the districts .................................................... 51 
Table 4. 2 Spatial Patterns of Malaria Incidence in the Districts............................................. 52 
Table 4. 3 Analysis of high/low clustering for different years using Getis and Ord Gi* 
statistics .................................................................................................................................... 59 
Table 4. 4 Summary of OLS result .......................................................................................... 61 
Table 5. 1 Summary statistics for OLS .................................................................................... 66 
Table 6. 1 Possession of Bed nets and Malaria incidence ....................................................... 76 
 
 
 
 
 
 
 
 
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LIST OF FIGURES 
 
FIGURES               PAGES 
Figure 2.1 Distribution of World Malaria ................................................................................ 13 
Figure 2.2 Map of Africa Showing Distribution of Plasmodium Falciparum Malaria ............ 15 
Figure 2. 3 Triangle of Human Ecology of disease ................................................................. 28 
Figure 2. 4 Adapted from human ecology of malaria framework. .......................................... 30 
Figure 3.1 Location Map of the Study Area ............................................................................ 34 
Figure 4. 1 Malaria Hotspot 2012. ........................................................................................... 54 
Figure 4. 2 Malaria Hotspot 2013. ........................................................................................... 55 
Figure 4. 3 Malaria Hotspot 2014. ........................................................................................... 56 
Figure 4. 4 Malaria Hotspot 2015. ........................................................................................... 57 
Figure 4. 5 Malaria Hotspot 2016. ........................................................................................... 58 
Figure 5. 1 Map of IRS as malaria control intervention. ......................................................... 68 
Figure 5. 2 Map of LLINs as malaria control intervention. ..................................................... 69 
Figure 5. 3 Map of Herbal Medicine. ...................................................................................... 70 
Figure 5. 4 Map of Anti-Malarial Drug as malaria control intervention. ................................ 71 
Figure 6.1 Respondents‟ perception about effectiveness about IRS........................................ 75 
Figure 6. 2 Respondents perception about effectiveness of LLINs ......................................... 77 
Figure 6. 3 Respondents‟ perception about effectiveness of antimalarial drugs ..................... 79 
Figure 6. 4 Respondents‟ perception about effective of herbal medicine................................ 81 
 
 
 
 
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ABBREVIATIONS 
 
ACT                               Artemisinin Combination Therapy 
ITN                                Insecticide Treated Bed Nets 
IRS                                 Indoor Residual Spray 
IPT                                 Intermittent Presumptive Treatment 
IPTP                               Intermittent Preventive Treatment of Pregnant Woman 
LLINs                             Long Lasting Insecticide Nets 
NMCP                            National Malaria Control Programme 
MDGs                             Millennium Development Goals 
OPD                                Out-Patients Department 
RBM                               Roll Back Malaria 
WHO                              World Health Organisation 
OLS                                Ordinary Least Square 
IDW                                Inverse distance Weighted 
GWR                              Geographically Weighted Regression 
GIS                                 Geographic Information System 
CBO                               Community-Based Organisation 
NGOs                             Non-Governmental Organisations 
GHS                               Ghana Health system 
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PHC                               Primary Health Care 
WLS                              Weight Least Square 
 GIS                               Geographic Information System 
 
 
     
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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ABSTRACT 
 
The primary objective of this study was to examine the spatial distribution of malaria 
incidence in the Brong Ahafo Region by using a GIS as an analytical tool. 
The research employed the cluster based sampling method to solicit the views of 245 
households using a questionnaire. Malaria incidence indicated a variation from one household 
to another and from one administration district to another. Malaria clusters were determined 
using Getis and Ord statistic. This cluster analysis indicated that malaria distribution, 
characterized by areas with high malaria incidence, called hotspot areas, with a moderate 
malaria incidence known as not significant and areas of low malaria known as cold spots. 
Malaria causing factors in the study area were also determined using Ordinary Least Square 
Regression. Graphs were also used to illustrate the effectiveness of implementing 
interventions in the Brong Ahafo Region. Malaria control interventions that were used in the 
study area consisted LLINs, IRS, Antimalarial drugs and Herbal medicine. The relationship 
between malaria incidence and interventions were assessed using Geographically Weighted 
Regression. The Geographically Weighted Regression proved that high malaria districts have 
many interventions programmes.  
The study indicated that 76% of the respondents revealed that Brong Ahafo Region is an 
endemic area due to its internal geography which aids in the development of the mosquito. 
Malaria Hotspot maps from 2012-2016 were created in this study using the Getis and ord 
Gi*statistics. The analysis of high/low clustering for different years indicated that the 
concentration of clustering in 2013 with z-score of 12.0161 which was the highest. The study 
showed that elevation has a negative relationship with malaria incidence hence the null 
hypothesis was rejected. The research indicated that 79.2% of the respondent understood that 
malaria distribution can be controlled through effective interventions like LLINS, IRS, and 
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antimalarial and herb medicine while 20.8% assumed that clearing of the vegetation and 
drainage of gutters. Malaria interventions such as LLINS, IRS, antimalarial and herbal 
medicine found in many districts hence the null hypothesis was rejected. The study revealed 
that LLINS was the only intervention that recorded a positive coefficient with a t-statistic less 
0.05. Based on the effectiveness of interventions, the study showed that LLINs were ranked 
as very strong, the IRS was ranked not effective, antimalarial was ranked with regard to the 
drugs that were ACTs (76%), Quinine (20%) and Amodiaquine (4%) as well as herbal 
medicine was ranked effective in controlling malaria. 
 The spatial clustering and risk maps have been used to show malaria incidence, distribution 
and demonstrated as an efficient tool for quick identification hot and cold spots of malaria 
incidence. This can provide helpful insight into the underlying means driving incidence and 
major factors influencing malaria in Brong Ahafo Region.   
Key words: GIS, Malaria, Interventions, Brong Ahafo Region 
           
           
           
           
           
           
           
     
 
 
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CHAPTER ONE: INTRODUCTION 
1.1 Background  
 
Malaria is one of the world‟s most devastating vector-borne diseases and an important cause 
of morbidity and mortality in developing countries, representing a major health burden 
(Sachs et al., 2002; Guinovart et al., 2006; Alonso et al., 2013). Looking for causes of malaria 
began in the middle-ages, yet, it still exists as an important health burden. The word „Malaria‟ 
comes from the Italian words „mala‟ and „aria‟ meaning „bad air‟ (Carter & Mendis, 2002). 
Malaria is a vector-borne human parasitic disease caused by the female anopheles mosquito. 
It is transmitted to people through the sting of infected female anopheles mosquitoes (WHO, 
2016). Risk variables like the parasite, the mosquito vector, the human host and the 
geographical setting are determinants that cause malaria spread. Malaria continues to be a 
significant parasitic disease in the world.  Regardless of the birth of a Malaria Control 
Programme in several parts of the globe for the past decades, the influence of malaria on 
humans persists, particularly, in most of the tropics and subtropics (WHO, 1998; de Souza & 
Riley, 2002; Sachs, 2002; Sachs & Melaney, 2002; Koremomp et al., 2003). 
Globally, almost 3.2 billion people live in malaria prone areas. Regardless of 100 years of 
malaria control, in 2014, 97 nations worldwide recorded malaria infections (WHO, 2013). In 
2012, malaria occurred in 104 countries (World malaria report, 2012). In tropical and 
subtropical regions of the world malaria are prevalent, including parts of Africa, Asia and 
America. In Africa, the economic effect of malaria is estimated at $12 billion every year 
(Gallup & Sachs, 2001). Malaria is not pleasant for commerce; the disease is accountable for 
the rise in health care expenditure, reduction in productivity, and employee tendency to be 
away from work, which has unfavourable influence of a firm‟s image (WHO, 2011). 
Generally, countries in Africa lose up to 25% of revenue to malaria (WHO, 2008). The 
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financial cost involved in accessing good health care is an obstacle to the majority of people 
who have been infected by malaria (Egunjiobi, 1983; Baume et al., 2000; Mugisha et al., 
2002; Noor et al., 2006). The emergence of the disease has led to powerful adverse 
relationships to the economic growth of households in the affected countries. The costs to 
individuals are: financial cost which reflects the unit cost of an intervention, an additional 
cost of travel time, time spent in the hospitals or clinics, as well as poor quality of care 
received from the health providers. Malaria mortality is approximately 80% among children 
less than five years and pregnant women in Africa (WHO, 2008). In Africa, malaria infection 
among pregnant women is nearly 35 million each year (WHO, 2013). The dangerous effects 
of malaria infection during pregnancy include maternal anaemia, intrauterine slow 
development, premature birth and stillbirth (McGregor, 1984; Bader et al., 2010 and Yetich 
et al., 2010). 
Previous studies on malaria have used various designs and determinants. Some examined the 
correlation between socioeconomic status and malaria. In this regard, age, vulnerable 
individuals and disease immunity are common determinants. Specific vulnerabilities include 
pregnant women whose immunity has been reduced due to first or second pregnancies, young 
children who have not fully developed immunity against malaria and migrants from areas 
with no or little malaria transmission. Malaria in school children is a significant reason for 
absenteeism in disease prone areas. It is estimated that about 2% of children who survive 
cerebral malaria are likely to sustain brain damage (WHO/UNICEF, 2003). Therefore, the 
regular occurrence of malaria among young children can have an adverse influence on their 
educational success. This is a danger to human capital growth which is a major component in 
economic development. 
One study stated that still only a handful of poor people with  lower socioeconomic status  
budgeted and purchased Insecticide Treated Nets (ITNs), and the richer segments with higher 
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social and economic status were four and half times  more likely to buy ITNs and use other 
control measures (Howard et al., 2003). It has been identified that the poor social and 
economic communities are restrained from buying bed nets, mosquito coils, repellents and 
insecticide sprays. The story is not same for the richer segments as expected (Ahmad, 2012). 
Treatment seeking behaviour of an individual infected with malaria has a relationship with 
the person‟s social and economic status. Another study identified that the poorest households 
use self-diagnosis as their means of intervention, while the less poor households usually use 
traditional healers and community health workers for treatment of malaria (Uzochukwu & 
Onwujekwe, 2004). A strong correlation exists between socioeconomic status and malaria 
morbidity and mortality since many lower socioeconomic households need appropriate 
preventive measures. Malaria, consequently, is not just a health challenge; it is an economic 
growth challenge. Besides the adverse impact on the economy at the national level, malaria 
was threatening effects on the development of tourism, investments and trade in most prone 
areas. 
In Ghana, malaria occurrence per direct observation of patients is around three cases per baby 
per year. Approximately 44% of out-patients at health facilities is because of malaria and thus 
considered as the most important cause of hospital or clinic attendance. Malaria mortality is 
25% among children less than five years; malaria causes anaemia among pregnant women 
and placental parasitaemia which place the mother and fetus in serious danger (GHS/NMCP, 
2004). Ghana Health Service (2004) reported that malaria is the principal source of mortality 
in the health organization with its death rate at 17.1%.  In 2008, Ghana recorded total out-
patient morbidity in 5,041,025 constituting 47.4% of the total death recorded as a result of 
malaria (Anon., 2010). In Ghana, the National Malaria Control Programme (NMPC, 2013) 
reported about 11.3million cases of the outpatient malaria. The National Malaria Control 
Programme in Ghana aims at reducing malaria mortality and morbidity by 75% by the year 
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2015 as a way of achieving the Millennium Development Goal (MDGs). A joint report from 
WHO-UNICEF (2015) stated that in Ghana the malaria mortality ratio has been reduced by 
60% since the emerged of the MDGs in 2000, and this is converted into 6.2 million lives 
protected and children being the greater number. In 2013, Brong Ahafo Region recorded 1, 
357, 000 malaria cases which was the highest rate of cases in Ghana (NMCP, 2013). 
Several interventions have been implemented in the form of policies and strategies to control 
malaria. Common control measures are categorized as an Insecticide Treated Bed Nets 
(ITNs), Indoor Residual Spraying (IRS), diagnosis and treatment as well as Intermittent 
Preventive Therapy (IPT) (WHO, 2009). 
 In 2009, 39 regions in the WHO African division out of 83 regions have had ITNs free of 
charge. The IRS, with the WHO legalized chemical like DDT continues to be one of the key 
interventions for lessening and breaking off malaria distribution by vector control in the 
environment. WHO (2009) reported that 71 regions along with 27 in the African region put 
into effect IRS and 17 regions reported utilizing DDT for IRS. The ITP is suggested for 
groups of people in regions with elevated distribution that are specifically sensitive to 
acquiring malaria or depressed immunity because of its occurrence, especially among 
pregnant women and infants. WHO (2008) reported that 35 out of the 45 regions in Africa 
had approved IPT for pregnant women (IPTp) as a national plan. However, no country has 
approved the utilizing of IPT for infants (IPTi) as national plan. 
Malaria incidences in different African nations have declined owing to productive malaria 
resistance drugs, improvement in these drugs and major advancements in control initiatives 
(Protopopoff et al., 2013). Currently, the ultimate implementation processes for malaria 
control persist with measures to control anopheles mosquitoes. The control initiatives are 
aimed at every point of the spread pattern of malaria i.e. the relation between anopheles 
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mosquitoes, the parasite and the host (Gosoniu, 2008). The suggestions made by WHO 
(2012) on malaria control and prevention are IRS with insecticide, Long Lasting Insecticide 
Nets (LLINs), larvae control, preventive chemotherapy and early diagnosis and treatment. 
The emergence of the IRS as a malaria control by WHO in 1957 has been considered as the 
most effective malaria control in history (Najera, 1989). Currently, it is considered a 
significant instrument in controlling malaria (RBM, 2008; WHO, 2006 and WHO, 2012). The 
LLINs provide protection to people sleeping in order to avoid mosquito bites (WHO, 2005). 
This has decreased the rate at which infected mosquitoes that feed on human population, 
while sleeping or resting (WHO, 2008) helping to decrease malaria spread. Early diagnosis 
and treatment is needed to decrease mortality owing to malaria. The Artemisinin-based 
combination therapy (ACT) has been actively promoted by WHO for simple malaria 
infection. Larvae management is different from the IRS and LLINs, in spite of the efficiency 
of these instruments (Utzinger et al., 2001 and Gu et al., 2006). Larvae control has brought 
about malaria elimination in few areas of the globe (Kitron & Spielman, 1989).  
In geography, there are many approaches for research work, including the use of statistics and 
cartography. Tools such as GIS, Remote Sensing and other Geospatial techniques have aided 
in solving problems of malaria epidemiology and several other diseases (Getis et al., 1992; 
Booman et al., 2000; Boulos, 2004; Yeshiwondim et al., 2009). Geographic Information 
System is a computer-based technology for capturing, storing, processing and visualizing 
spatial data (Booman et al., 2000). The technology has the ability to merge the attribute data 
of disease incidence and other attribute characteristics with images such as topographical 
maps, satellite images and aerial photos. Geographic Information System has been used 
globally in producing disease distribution maps and for examining spatial pattern in disease 
distribution (Cattani et al., 2001). The introduction of GIS has contributed to malaria control 
programmes and it is currently considered a significant instrument in the processing and 
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carrying out of plans for the programme. In determining epidemiological patterns of disease, 
GIS contributes supplementary ability to carry out spatial and temporal analysis of health 
data. The cost involved in using the GIS application still serves as a barrier to it extensive 
usage (Booman et al., 2000). Geographic Information System supports a geographical 
database comprising environmental, social and health data. These data sets support the action 
of analytical examination. For instance, GIS can aid in the analytical examination of 
contagious disease data and spatial data and at that produce instant layout of the hot spots in 
the region of interest (Kolivras, 2006). 
Presently, malaria control and prevention programmes in Ghana involve the allocation of 
ITNs, IRS, and case management and prevention chemotherapy for pregnant women. 
However, the forms and dimension of interventions need to be based on the incidence and 
prevalence of disease in large populations and thus detect the source and cause of epidemics 
of the infectious disease occurrence. For example, maps have been acknowledged as a 
significant instrument for modelling malaria control programmes for years (Kleinschmidt et 
al., 2000 and Yeshiwondin et al., 2009). In the study region, the absence of malaria risk maps 
gives rise to challenges to make the best of the insufficient resources in accomplishing goals 
at the time of control programmes. Risk maps of the spatial distribution of malaria will be 
critical instruments in mediating and testing control programme. 
Therefore, this study examines the geographical distribution of malaria incidence in the 
Brong Ahafo Region and the interventions for malaria control using a GIS approach. 
1.2 Problem Statement 
 
In spite of numerous efforts to slow down malaria incidences, it is still one of the greatest 
threats in Africa. About 261 million malaria incidences were reported globally in 2010 with 
an Africa accounting for 81%. Also, malaria kills 655,000 persons each year with 91% of the 
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morality rate from Africa (World Health Organisation, 2011). In Ghana, the National Malaria 
Control Programme in the first quarter of 2016 reported about 2.2 million suspected cases of 
malaria, which represented a 3.50% increase  from the malaria incidence in 2015 (NMCP, 
2016). Key causes for the high malaria incidence in Ghana are attributed to poor quality of 
environmental sanitation which is strengthened by fast unplanned urbanisation and climatic 
factors (Mwangangi et al., 2012).  
Urbanization contributes to the prospect of development and growth in an area, but it is 
accompanied by some adverse effects. These adverse effects include weakening conditions of 
the health of people owing to unsanitary conditions, lack of social amenities and the need for 
land for building sanitation facilities. Chaplin (1999) and Songsore (2003a), indicated that the 
major risk confronting several developing nations undergoing urbanization is the problem of 
sanitation. As the population of a specific area increases, sanitation problems are elevated, 
leading to acceleration of infectious disease like malaria. 
In Ghana, environmental problems like poor sanitation, pollution of water bodies and 
inappropriate disposal of waste are among the topmost problems that the country faces 
(Government of Ghana, 2010a). The Brong Ahafo Region is no exception from the rest of 
Ghana. It is faced with crucial environmental problems such as inappropriate disposal of solid 
and liquid waste, dirty drains and air pollution resulting from the smoke from burnt refuse. 
The choked drainage systems with waste and stagnant water in receptacles serve as breeding 
grounds for mosquitoes and hence the endemicity of malaria. 
Much effort has been made by the government of Ghana and Non-Governmental 
Organizations in the health sector to eliminate malaria in the Brong Ahafo Region. 
Nonetheless, the incidence rate remains high. This has triggered the question „why is the 
malaria incidence still high in spite of the many interventions‟? The continuous growth in 
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malaria incidence is of great concern to the people of the Brong Ahafo Region because this 
elevated level of malaria exacerbates poverty levels. This has resulted in loss of work hours 
and the high economic cost due to treatment (UNICEF, 2007). However, an examination of 
malaria incidence and control in the Brong Ahafo Region has not received much attention in 
academic research. Regional malaria studies for site specific incidences, resistance to 
insecticides and anti-malarial drugs, malaria hotspots and health system responses are 
lacking. Asante et al. (2011) studied malaria epidemiology in the Ahafo area of Ghana. The 
research did not address how the malaria incidence differs between districts in the whole 
Region. It rather assessed malaria epidemiology in special areas such as mining areas in the 
Brong Ahafo Region as a way to control the disease. Osei and Yibile (2015) reported the 
geographic pattern of malaria in the Brong Ahafo Region. In this work, the yearly 
geographical distribution of districts‟ level malaria incidence from 2008 to 2011 in the Brong 
Ahafo Region of Ghana was mapped. However, the research did not report on the correlation 
between malaria incidence and interventions. Daniel (2009) studied malaria transmission 
dynamics and Pyrethroid Insecticide Resistance status of anopheles Gambiae Senue Lato 
Gilles in two districts of the Brong Ahafo Region. This study unveiled some findings on 
malaria transmission that will assist in providing better malaria control. However, this 
research was limited in assessing the effectiveness of malaria control intervention. To fill 
these gaps, this current research aims at examining the spatial distribution of malaria 
incidence by identifying major factors facilitating conditions for mosquito breeding and will 
produce malaria risk maps to determine hotspot areas, which will support decision-making 
concerning malaria monitoring and control. 
 
 
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1.3 Research Questions 
 
 How is malaria incidence spatially distributed in the administrative districts? 
 Are there correlations between malaria incidence and intervention programmes? 
 What are the malaria control interventions and their effectiveness? 
 
1.4 Aim and Objectives 
 
The aim of this research is to examine the spatial distribution of malaria incidence in the 
Brong Ahafo Region by using a geographic information system as an analytical tool. 
The specific objectives of the study are to: 
 Examine the spatial distribution of malaria incidence. 
 Assess the correlation between malaria incidence and interventions in the study area. 
 Identify malaria interventions existing in the study region and their effectiveness. 
1.5 Research Hypotheses 
 
Hypothesis 1: 
Hₒ=High malaria incidence districts are not likely to have many intervention programmes. 
H₁=High malaria incidence districts are more likely to have many intervention programmes. 
Hypothesis 2:  
Hₒ= Elevation is the main determinant of malaria incidence in the study area.  
H₁= Elevation is the not main determinant of malaria incidence in the study area.  
 
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1.6 Rationale of the Study  
 
The research was expected to examine the spatial distribution of malaria incidence and 
interventions in the study area. Likewise, information on the distribution of malaria incidence 
related to interventions for malaria control was sought. Data on climatic variables and on 
malaria transmission‟s spatio-temporal distribution from objectives 1 and 3 were used to 
determine malaria distribution (hot spots). Interventions such as IRS, LLINs and 
environmental management were related to distribution of malaria to determine if malaria 
transmission is low or high in these areas. The interventions were expected to greatly 
decrease malaria vectors with an ultimate reduction of malaria transmission using small 
resources and cost.                                                          
1.7 The Scope of the Study 
 
The scope of this research was defined by the geographic area, the subject matter and the 
time frame. Geographically, the research was designed to cover the Brong Ahafo Region, 
which involves 11 health administrative districts out of the 27 districts in the region. 
Regarding the subject matter frame, the focus was on how to determine hot spot regions, 
what account for these hot spots and identify related interventions to control malaria in these 
hot spot areas through the application of GIS. For this research, the time frame for malaria 
cases is from 2012 to 2016. 
1.8 Organization of Thesis 
 
This thesis is organized into seven chapters. The first chapter presents introduction, problem 
statement, objectives, hypotheses, significance and scope of the study. The second chapter is 
the review of relevant literature. Malaria conceptual frameworks based on the literature were 
produced. In terms of methodology, this is captured in Chapter Three; the research links both 
qualitative and quantitative methods but is skewed towards a quantitative approach. The 
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chapter entails the comprehensive research design in choosing a study area, data collection 
and analysis, as well as methods used to address the research questions stated in the 
introduction.  
Chapter Four treated the first research objective through the use of GIS to examine incidence 
distribution. Under this chapter the Getis and Ord (hot spots) were used to examine malaria 
hotspots in the study area. This chapter also predicts the major factors influencing the pattern 
of malaria distribution within the study area. 
Chapter Five assesses the relationship between malaria incidence and interventions to control 
malaria. The use of the Ordinary Least Square tool was to produce a global model of the 
relationship between malaria incidence and existing intervention for malaria control. Chapter 
Six examines the various activities of malaria control interventions in place in the study area. 
It also treats the perceptions of respondents on the effectiveness of malaria control 
interventions, challenges and coping strategies. The final chapter of the thesis discusses the 
researcher‟s contributions to knowledge and future research aspiration. 
            
 
 
 
 
 
 
 
 
 
 
 
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CHAPTER TWO: LITERATURE REVIEW 
 
 2.1 Introduction  
 
Africa retains a dominant share of the global malaria burden. In 2015, the region had 88% of 
malaria cases and 90% of malaria mortality world-wide (WHO, 2015). However, much 
research is required, as previous studies have centred on medical and clinical aspects and not 
so much on its geography. 
Manifestations from the literature disclose multiple discussions that have suggested 
explanations between malaria, human and environment disease relationship. In this section, 
the thesis covered broadly the ecology of malaria where common interactions influence the 
disease. It encompassed the global malaria distribution, malaria situation in Africa and 
Ghana, conditions necessary for the anopheles mosquito development. It also covered the 
anthropogenic factors affecting malaria transmission, the use of GIS technology in malaria 
study and malaria control interventions.  
2.2 Global Malaria Distribution   
 
 Malaria is one of the world‟s most devastating vector-borne diseases and an important cause 
of morbidity and mortality in developing countries (Sachs & Malaney, 2002; Guinovart et al., 
2006; Alonso et al., 2013). Regardless of 100 years of malaria control, the malaria epidemic 
continues in 104 countries (World Malaria Report, 2012).  Figure 2.1 depicts the global 
distribution of malaria in Africa, South America, and Asia. Mediterranean, South Eastern 
Asia and Western-Pacific are quite in malaria endemic zones. In 2011, the world recorded an 
estimated of about 216 million malaria cases and almost 665,000 deaths. In 2012 
international funds raised for malaria control elevated from less than US$ 100 million in 2000 
to US$ 1.84 billion in the same year (WHO, 2012).  
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Although the number of zones where malaria transmission happens is estimated to have 
declined from 1900 to 2010, with the population at risk of contracting malaria has grown 
from 890 million to 3,410 million globally. The areas at risk were estimated from 77 to 39 
million Km² (Snow et al., 2005).   The people of South-East Asia and the Western Pacific 
have the greatest malaria risk. The people at risk are located in rural communities in or near 
the forested and border zones. These happen not to be the only places where the malaria 
vector exists in abundance, but places where availability of health care is lacking (Snow et 
al., 2005). From the year 1990 to 2010 an estimated number of malaria cases a year that lived 
with disability (YLDs) was respectively 433 and 498 per thousand of YLDs at all ages (Vos 
et al., 2012). Malaria is a massive burden in the world and troubles any progressive 
development of the world economies (Sachs et al., 2002).   
 
 
 
 
 
 
 
 
 
 
 
Figure 2.1 Distribution of World Malaria (Source: World Health Organization, 2012) 
 
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2.3 Malaria Situation in Africa 
 
Malaria is believed to have been emerged from Africa and to have coexisted with its 
definitive hosts, mosquito vectors and intermediate hosts, placental mammal (Poinar, 2005). 
The trace of mosquitoes can be found from 30 million years ago, indicating that the mosquito 
vector existed before the ancient history of man (Poinar, 2005). The ancient migrants might 
probably have introduced the Plasmodium from Africa to other sections of the globe, a 
circumstance that persists as tourists and migrants today return home with malaria from prone 
areas (Newton & White, 1999). 
Low-income nations of the globe are the most seriously affected by malaria disease (Mhalu, 
2005), with the majority found in Africa (Ricci, 2012). According to Dzinjalamala (2009) 
nine out of ten deaths due to malaria occur in Africa. Figure 2.2 is a map of Africa depicting 
the nations where malaria is endemic. The uniform Plasmodium falciparum spread areas in 
Africa were estimated to be inhabited by 753 million people, in 2010. Regions of low, stable 
transmission were estimated for 228 million people and have lasted for a definite period of 
time at the Horn of Africa, Sudan, Kenya, upland regions of Tanzania, Rwanda, Burundi, the 
Democratic Republic of Congo and Madagascar. This cut across the southern part of the 
stable transmission region in Angola, Zambia, Botswana and South Africa. The highest 
transmission areas of Plasmodium falciparum are mainly West Africa and large areas of 
central Africa are stretching out eastwards through Mozambique and Madagascar. It 
embodies 327 million people at risk. Ghana is among the risk areas with the malaria 
incidence greater than 5% but lower than 40%.  
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Figure 2.2 Map of Africa Showing Distribution of Plasmodium Falciparum Malaria     
(Source: The Malaria Atlas Project, 2010) 
2.4 Malaria Situation in Ghana  
 
Malaria is the single most significant disease in Ghana since 1985, being partly responsible 
for 45% of all outpatient cases at health facilities. It also contributes to 15% of mortality 
every year (GHS, 2004a). In Ghana, almost all malaria cases are caused by Plasmodium 
falciparum, which spreads through the mosquito bite. Malaria cases have been recorded for 
about 36% of patients admitted to health facilities for a decade (GHS, 2004a). In 2008, Ghana 
recorded total outpatient morbidity of 5 041 025 constituting 47.4% of the total recorded as a 
result of malaria (Anon, 2010). In Ghana, the effect of malaria continues to be immense for 
about 323 per 1,000 cases recorded among children less than five in 2008, with insufficient 
proof of the reduction from 2002-2008 (WHO, 2009). 
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The effect of malaria is complex in the forest zone of Ghana (Ahmed, 1989). Owusu-Agyei 
(2009) conducted a research, in the Brong Ahafo Region of Ghana on epidemiology of 
malaria in the forest-savanna transitional zone. The study accounted for increased spread of 
malaria due to the environment which helps in the development of the vector. Also, the 
parasite at their breeding sites with the elevated humidity that continue for a long time. 
The high social and economic expenditure of malaria treatment shows the need for greater 
success in malaria control interventions a way to reduce malaria incidences and deaths 
(Asante & Asenso-Okyere, 2003). Malaria control interventions have focused on the use of 
LLINs, IRS, drug use, case management and other vector control strategies (WHO, 2000, 
2005a, 2006). 
2.5 Conditions Necessary for the Anopheles Mosquitoes Development  
 
The ability of the anopheles mosquito to exist and increase in density is as a result of suitable 
climatic factors (Service & Townson, 2002). Climatic factors such as rainfall, temperature 
and relative humidity are the significant variables for the mosquito‟s development and growth 
but the most persistent one is temperature. According to Paaijmans et al. (2010), the humidity 
content from rainfall affects the malaria distribution, whereas temperature serves as the 
checking point for determining density level and the existence of the anopheles mosquito. It 
is unquestionably demonstrated that the anopheles mosquitoes are pleased to exist between 
16⁰C and 35⁰C, while the anopheles can exist with a relative humidity as low as 50% 
(Macdonald, 1957; Craig et al., 1999; Service & Townson, 2002). This explains the 
anopheles development containing between 9 to 10 days in a period of this temperature 
range.  
Further, Paaijmans et al. (2010) state that at a temperature between 20⁰C to 25⁰C associated 
with a 60% relative humidity, this climatic condition aids the longevity of mosquitoes to 
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elevate in density and in malaria incidence due to the increased number of days from 10 to 
14. However, as low or high as temperatures between 16⁰C and 35⁰C and low-lying relative 
humidity at 50%, deaths elevate and the longevity period of mosquitoes reduces to 9 days 
(Craig et al., 1999). A temperature over 40⁰C, the longevity estimate mark is assumed to be 
zero (Craig et al., 1999; Okogun et al., 2005; Fournet et al., 2010). This leads to a reduction 
in the density of mosquitoes, indicating a decrease in malaria occurrence. Numerous studies 
have analysed the interrelation between climatic factors and the Anopheles mosquito as well 
as significant determinants that have elevated their density, development and growth leading 
to possible high malaria occurrence. These studies accordingly acknowledge the necessity of 
research into other features such as the surroundings. And also the human being impact at a 
remote spatial scale and their place in the malaria climate relationship. 
The intention of this research and literature review is based on identifying the twisting 
interrelations between the major risk variables of climate and malaria. 
2.5.1 Temperature  
 
Temperature performs an important task in the transmission process of the parasite and the 
mosquito‟s longevity (Brooker et al., 2004; Mordecai et al., 2013). Temperature has a crucial 
impact on the development and longevity of the parasite (Blanford et al., 2013). The duration 
of the parasite relies on temperature and different climatic determinants (Martens et al., 
1995). The evolutionary process of the parasite in the mosquito is considered to stop at 16℃, 
and transmission at as low as 18℃, though these estimates or findings are insecure. However, 
a temperature higher than 22℃ is appropriate for secure transmission (Craig et al., 1999). 
Temperature affects the mosquito‟s existence as well. The maximum temperature for a 
mosquito‟s existence is within the interval of 20 ℃ and 25 ℃ (Martens et al., 1995), and 
malaria infection is at 25 ℃ (Mordecai et al., 2013). Also, it is established that the most 
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appropriate temperature for fast development of the mosquito vector is within an interval of 
20-30℃.  To some extent, increasing temperature lessens the range between the blood meals 
and intensifies the mosquito‟s biting rate, and hastens the growth of the parasite thereby 
increasing the malaria infection (Martens et al., 1995). An elevated temperature should 
intensify the chance of transmission because it lessens the period of time   between the 
infection and the outbreak of the disease‟s symptoms (Reiter, 2001). 
Studies have demonstrated that an extremely elevated temperature, approximately 40-42 ℃ 
can facilitate the death of the mosquito (Craig et al., 1999) and parasite (Dale et al., 2005).  
The day-to-day longevity of mosquitoes ranges from 0℃ to 40℃ (Craig et al., 1999). Low 
temperatures can decrease the quantity of mosquitoes in an area as a result of the extended 
larval period (Dale et al., 2005). Hence, temperature is regarded to be a central variable for 
malaria transmission (Gething et al., 2011).  In Madagascar, a research by Rakotomanana et 
al. (2010) for a year did not ascertain a specific relation between the average monthly 
temperature and clinical malaria cases at a max temperature degree of 26⁰C. 
As reported by, Tay et al. (2012) in a study based on a population study in Kumasi in Ghana, 
with the geographical environment the same as in the highlands of Ethiopia. In both cases, a 
research on the indirect effect of the high temperature on malaria incidence over a 12 week 
period was measured. The research also assessed the relationship between high temperature 
and malaria incidence in the broad scale population. These researches varied in their findings 
of the influence of temperature on malaria. According to Teklehaimanot et al. (2004)  a direct 
significant positive rise in malaria incidences came as a rise in the lowest temperature, while 
Tay et al. (2012) reported a direct opposite  relation, namely no  significant influence in an up 
to eight weeks period.  
 
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 2.5.2 Rainfall  
Rainfall plays a key part in malaria transmission (Kovats et al., 2001). The impact of rainfall 
on malaria spread is composite. The relationship that exists between malaria and rainfall 
patterns is well understood as the period when the temperature is not restricted or when 
sewage in geographical areas becomes a breed site for anopheles mosquitoes. Or in a 
situation where rains may bathe their breeding sites resulting in short-term break ups in their 
longevity and development.  It is difficult to understand the preliminary subtle relation 
between malaria and rainfall (Hoshen & Morse, 2004; Cator et al., 2013). 
Investigation of rainfall patterns has been employed to the forecast malaria epidemic. 
According to Shanks et al. (2002) and Teklehaimanot et al. (2004) the relation between 
rainfall patterns and malaria occurrence is not consistent contemporarily. The place where 
meaningful relations happen, the degree, chronological characteristics, minimal quantities of 
rainfall needed and time varies shown different results (Craig et al., 2004b; Ye et al., 2008; 
Oluleye & Akinbobola, 2010).   
Ye et al. (2008) state that the ultimate relation between rainfall and malaria happens at a 
specific start of the rainfall rate and is usually associated with a temperature over 23⁰C. 
According to    Craig et al. (1999), for constant transmission of malaria to happen, rainfall 
pattern should be at least 80mm for a period of five months associated with a temperature 
estimated to be nearly 22⁰C. Furthermore, Craig et al. (1999) asserted that rainfall over 
80mm within a month is inadequate for any meaningful spread of malaria regardless of the 
degree of temperature.  However, Ye et al. (2008) opposed Craig et al. (1999) argument, by 
stating that rainfall of 100mm within a month stimulates a meaningful spread of malaria, with 
a relation scale of 160mm   with a temperature between 23⁰C and 27⁰C. Fournet et al. (2010) 
equally recorded that minimum rainfall pattern as 2.3mm and 200mm as maximum pattern 
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and that a temperature over 23⁰C stimulates the spread of malaria throughout the year in 
Ouagadougou.  
The results may vary, but eventually a conventional mark for a rainfall measure may over- 
estimate its influence at a locality.  The results of  Ye et al.‟s (2008) research demonstrates 
that below the period of time between malaria and rainfall, a conventional mark of any 10mm 
of rains with a maximum  over 160mm, stimulate approximately a 4% malaria incidence 
increment below a 23⁰C temperature rate.  
2.5.3 Relative humidity (RH)    
 
Relative humidity (RH) appears to have a simple impact on the growth of the parasite, 
however, it may influence the life cycle of the mosquito (Dale et al., 2005). The maximum 
relative humidity for the mosquito‟s longevity is over 60% with a temperature ranging from 
20℃ and 30 ℃ (Bureau of Endemic Diseases Control of P.R. China, 1998). On the condition 
that the average relative humidity is over 60% (Bureau of Endemic Diseases Control of 
People's Republic of China, 1998) or over 80% (Bhattacharya et al., 2006), the longevity of 
the mosquito vector becomes shorter. In an event that the mean relative humidity is lower 
than 25%, mosquitoes will stop biting (Bureau of Endemic Diseases Control of People's 
Republic of China, 1998), the adult mosquito longevity will be significantly decreased by the 
low humidity (Keiser et al., 2002), hence malaria spread will be no more (Dale et al., 2005).   
Numerous researches have been administered to improve the understanding of relative 
humidity on malaria transmission. Okogun et al. (2005) argued that an increased temperature 
state, relative humidity aids, to decrease scarcity of mosquitoes‟ breeding sites, by that 
increasing their longevity and lifetime. As stated previously, the relation between malaria and 
mosquito densities have been indirect. Relative humidity is an efficient forecaster of malaria 
occurrence in different geographical regions and its results have been fluctuating like rainfall.  
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2.6 Anthropogenic Factors Affecting Malaria Transmission 
 
Apart from the impact of climatic conditions in malaria transmission, non-climatic 
determinants like human activities such as movement, environmental modification and also 
socioeconomic status, and vector control interventions influence malaria spread (Githeko et 
al., 2000; Lindsay et al., 1998; Reiter, 2001). The impacts of the non-climatic determinants 
influencing malaria transmission are shown below; 
Keating et al. (2005) and Deressa et al. (2007) and Peters (2010) asserts that beside the 
farming methods, pastoral farming can display problems of the anopheles mosquito turning 
into the vector. Peters (2010) indicates that based on the scale of pastoral farming and the 
great multitude of animals available in a zone, the greater the option for the generation of 
blood present for the mosquito and the higher the transmission of malaria at that level.   
Healthcare systems with accessibility, affordability, quality of care and efficiency with 
sufficient assistance in the global and national level have provided meaningful results of 
decreasing malaria mortality and morbidity.  
Aikins et al. (1993) and Opiyo et al. (2007) state that numerous researches current in Africa 
demonstrate that the spread pattern of malaria is not comprehended by the people and  creates  
a continuous space between consciousness and action (Opiyo et al., 2007). For example, 
Mwenesi et al. (1995) and Yadav et al. (2007) anticipate that enlightenment would elevate 
people‟s consciousness about the reasons of malaria spread. However, many maintain the 
misconception that the reason for the spread of a disease such as malaria is the act of witches 
and the rays from the sun. 
In numerous states in the world, especially in African countries, migration of people for 
political, social and economic reason creates the avenue for transmission of malaria. The 
increased movement of fugitives and immigrants may spread malaria in free malaria zones 
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owing to the introduction of new parasites which may be resistant to treatment in these free 
zones. However, sometimes immigrants may have the malaria transmitted when they access 
these malaria zones because they have migrated from low-lying or no spread region (Giada et 
al., 2003).   
2.7 The use of GIS Technology in Malaria Study  
 
Tools such as GIS, Remote Sensing and Geostatistical methods have aided in solving 
problems of malaria epidemiology and several other disease types (Getis et al., 1992; Boulos, 
2004; Yeshiwondim et al., 2009). The constituent part of a GIS contains not only a data bank, 
but also geographic information and a number of techniques that connect the systems as one. 
GIS has been explained as the branch of scientific knowledge in geographic information 
science (Goodchild, 1992a), which is clearly described as „research on the generic issues that 
surround the use of GIS technology, impede its successful implementation, or emerge from 
an understanding of its potential capabilities‟. Below are some examples of how techniques in 
GIS technology can be used in entering, storing, analysing and displaying digital Geo-
referenced data in research work.  
Thomson et al. (1997) assert that malaria mapping is based on the relationships between 
disease occurrence and climatic variables being natural elements which are suddenly 
impacted by meteorological determinants. So the connection can be employed to anticipate 
the malaria occurrence within localities where data are not obtainable. Wimberley et al. 
(2008) assessed the local and global distribution of malaria incidence in the USA. The 
research aimed at analysing the difference between non-spatial and spatial classic methods to 
determine the distribution pattern of malaria in a locality. The findings acquired demonstrated 
an integration of spatial dependency of the major variations in the evaluation of malaria 
allocation.         
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Yeshiwondim et al. (2009) conducted a study in  Ethiopia assessing 543 villages comparing  
the global and local  pattern of malaria  distribution based on information acquired from six 
laboratories and health care centres. The findings obtained indicated a difference in malaria 
occurrence in a way that was based on gender and age, where the quantitative data were 
meaningful on ages as low as five years or more. The local cluster of disease occurred in the 
space that separated villages based on meaningful area intervals. Malaria risk maps were 
created to indicate hot zones for easy observation and location marking. Tsai et al. (2009) 
conducted a study in Taiwan using the spatial autocorrelation technique based on Moran's I, 
local Getis and Ord statistics. The researches explained and illustrated spatial clusters within 
regions and could locate 20 dominant reason of mortality.  Basel (2008) examined the spatial 
trend of malaria distribution in Africa and evaluated the connection between malaria and 
ecological variables that differ progressively in space. Spatial autocorrelation was used to 
analyse the findings, which indicated a positive significant between malaria cases and 
ecological variables.    
The process of examining and determining the correct coordinates and to coordinate the 
system   to obtain data on unreferenced malaria cases with reference to environmental data, 
applying analytical approach, can aid in ascertaining the determinants that are partly 
responsible for malaria spread and death at distinctive geographical environment (Craig et al., 
2007).  
2.8 The limitations in using GIS for Malaria Research Studies 
 
The application of GIS in developing countries like Ghana has many challenges. These 
limitations are different in most countries due to variation in spatial setting. Lack of 
awareness, lack of qualified staff and financial implications of hardware and software are 
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some of the major challenges to mention. There is much connection between these 
limitations. 
GIS is quietly an expensive application in most developing countries. The financial 
implication of data collection, the monetary value of software and hardware and costs for 
qualified staff is over the ability of most developing countries in Africa. There are difficulties 
in data transfer due poor internet connection. The lack of corroboration from organizations 
and lack of awareness among heads of department of what GIS can aid to empower in the 
decision making process in Africa is demerit for the application. Hence, it is important to 
influence to elevate the awareness of governing bodies to convince them into allocating funds 
into the development of the application (Stuart et al., 2009). 
2.9 Malaria Control Interventions   
 
 Malaria incidence in different African nations has declined owing to productive malaria 
resistant drugs, improvement in these drugs and major advancements in control initiatives 
(Protopopoff et al., 2013).  
In recent times, the ultimate implementation processes for malaria control persist with 
measures to control anopheles mosquitoes. The control initiatives aim at every point of the 
spread pattern of malaria that is the relation between anopheles mosquitoes, the parasite and 
host (Gosoniu, 2008).  
Mabaso et al. (2004) state that major control initiatives are put in place to decrease the 
relation of the human-vector connection. The decline in the longevity of the mosquito vector 
influences its duration to spread parasites. Delves et al. (2013) analysed the possible 
associations that influence measures to stop parasites being transferred from host to 
mosquito. Alonso et al. (2011) conducted a study in southern Africa, on IRS and LLINs 
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indicating the advantages of fundamental control initiatives of the mosquito vector in the 
region based on WHO suggestions (Mabaso et al., 2004).  
The suggestions made by WHO emphasise an instant identification of the disease, quick 
treatment by the discovery and the sudden spread of the malaria occurrence. Gosoniu (2008) 
and Craig (2009) established that no malaria incidence, host or health care methods are 
uniformly allocated, and measures are required to aim at the people of the dominant risk 
regions in time. The initiatives for malaria control change based on the understanding of how 
the disease spreads and can be controlled, while the means required to measure timing 
conditions at risk times to assess and understand the unique environments of spread in order 
to accomplish the appropriate results are vital.   
In reaction to the malaria, plague the World Health Organization (WHO) approved the Global 
Malaria Control Strategy (GMCS). The Ministry of Health initiated a general Malaria Control 
Action Plan (MCAP) in November 1992 after Ghana had accepted the Global Malaria 
Control Strategy (GMCS), with the intention to decrease the incidence of malaria   and death 
in the country (Ahorlu et al., 1997).  
The Government of Ghana employed the „Roll Back Malaria (RBM) Action‟ in 1999 and 
established a vital plan to assist its operation. The Ghana RBM highlights the intensification 
of health services by means of an alliance with the United Nations Children‟s Fund 
(UNICEF), the United Nations Development (UNDP), the World Bank, ministries, NGOs, 
the private sector, as funders of research institutes to bring about treatment and preventive 
action to a greater extent. The aim of the initiative was to decrease malaria related incidence 
and death by 50% from the 2010. Eventually, the Malaria Control Strategic Plan emerged in 
2008-2015, with the aim at declining malaria incidence and death by 75% by the year 2015 
and as a way of achieving the Millennium Development Goals (MDGs). This objective was 
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to be attained by the means of a general health sector growth, an advanced significant 
investment in malaria prevention and to extend the scope of availability of malaria treatment 
and prevention interventions. 
2.10 Summary of literature review 
 
This study aims at a re-examination of research works on malaria; it indicates the relation 
between climate-malaria researches, which happens to be a subtle and complicated 
connection that exists between mosquitoes, malaria occurrence and meteorological 
determinants and the function of temperature in the general interaction processes. The results 
of research work reviewed previously indicate that temperature has several measurement 
forecasters and has proved to be more consistent in determining its connection with malaria 
occurrence based on the assessment of different fundamental meteorological determinants 
like rainfall and relative humidity.  
The reviewed literature indicates that climatic factors are uniformly meaningful under other 
conditions of study as assessments show the temperature is self-consistent, meaningful and 
perhaps the master forecaster of the relationship between malaria and climate on a 
geographical scale and place.  The question that remains challenging and to be answered is 
the applicable means of resolving the environmental problem of the case‟s research locality. 
 
2.11 Conceptual Framework 
 
Malaria in the forest regions of Ghana can be observed as an exclusive combination of 
environmental conditions and social reactions which is caused by ecological, political, 
economic and cultural circumstances. Recent methods to curb malaria demand a combination 
of ecological and social methods, including societies and government. These methods depict 
an ecological context of health (Curtis, 1996) indicated in the description of ‟health as a state 
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of complete physical, mental and social well-being and not merely the absence of disease or 
infirmity‟ (WHO, 1986). This idea of health indicates system interactions between 
populations and their ecosystem, specifically between economic determinants, together with 
physical and biological surroundings. The variables evaluated in the study were obtained 
from these models: The ecological model and the human ecology of disease (Meade and 
Emch, 2010) 
An ecological paradigm portrays interaction and negotiation, serving to connect people, their 
conduct and their social and geographical setting through various places and a time scale. The 
main fundamental law of the theory is that human mutual relations and conduct in the social 
arena and the varied distances as their impact on health developments is rooted in 
individuality, insight and resources. Therefore, the degree of agreement serving to connect 
people and their many stages of social and artistic settings is a significant call of well being 
(Stokol, 1996; Butterfield & Lewis, 2002). Even though this model is effective for analysing 
health behaviour, it puts more stress on the social environment than the natural environment. 
Numerous researches have used the ecological models in malaria study, malaria Treatment-
Seeking Behaviours (TSB) and malaria Knowledge, Attitude and Practice (KAP) which are 
originally interested in testing how factors of development, assets and artistic impact on 
understanding and usage of malaria intervention work (Panter-Brick et al., 2006; Esse et al., 
2008; Oresanya et al., 2008). These researches have utilised the mixed method approach in 
several areas, therefore being spatially accurate. Oresanya et al. (2008), evaluated the 
function of many features impacting the understanding of insecticide-treated bed nets (ITN) 
with the focus on estimating development concerning malaria in the Millennium 
Development Goal (MDG) and discovered that the utilisation of ITNs within an area is 
influenced by religion, income, education and ethnicity (Noor et al., 2003). 
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Human ecology of disease relates to the mode in which human behaviour, in its cultural and 
economic settings interrelates with geographical region to generate and overt disease in 
vulnerable people. These interrelations   determine the success of the numerous determinants 
showing the integration of „habitat‟, „population‟ and „behaviour‟ (Meade & Emch 2010, p. 
30).  
 
Figure 2. 3 Triangle of Human Ecology of disease (Source: Meade and Earickson, 
(2000). 
 
                                                                                            
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According to Gatrell and Elliot (2009), habitat is „space‟ for the mosquito vectors and 
humans. Habitat is made up of physical, built and social environment. The physical 
environment is connected with determinants that biologically promote the growth and 
development of mosquito vectors like climate, soil composition, as the social, environment 
determinants constitute of political and social patterns like administrative government, school 
and health care service. The built environment determinants are housing types and workplace.  
Population is associated with the special qualities of humans as natural organisms which 
serve as the possible host of diseases. Population‟s special qualities involve genetic traits and 
gender, which explain the human capability to encounter varying settings and disease 
condition such as malaria (Meade & Emch, 2010). As stated by Agomo et al. (2009), Phillips 
et al. (2009) and WHO (2012a), children less than five years, pregnant women and non-
immune migrants staying in geographical areas prone to malaria have been categorized as the 
more sensitive people. This is due to fact that pregnant woman‟s immunity has been lessened 
as a result of the first or second pregnancies, children less than five have not developed 
immunity against malaria and the non-immune migrants come from areas with no or little 
malaria transmission. Nahum et al. (2010) assert that on a genetic basis, people without sickle 
cell probably suffer the effect of malaria more than the sickle cell carriers. 
MacCormack (1984) and Brown et al. (1996) stated that humans as cultural creatures 
participate in day–to-day behaviour that changes their environment. They modify the 
environment to suit them through their belief systems defined by values, norms and insight 
by creating homes, schools, partake in cultural and religious activities, and apply new 
technology and so on.  According to Meade and Emch (2010) these describe what is meant as 
behavioural variables, which explain the visible portion of human culture. They single out 
four ways that show that interaction exists between humans: vectors, parasites and 
geographical area which have health consequences. The earliest one among the four is 
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through human modification of the environment, in the process of humans creating an 
ecological condition, thereby generating the breeding spots of the anopheles mosquito. 
Secondly, partaking in unhealthy conduct. Thirdly is the process of disease diffusion in the 
environment and the last is based on the cultural belief systems, norms and perceptions. In a 
process to gain knowledge of the interrelationship between humans, diseases and the 
environment is by investigating and quantifying the connected determinants and thereby 
testing their interaction. Although, they are frequently very difficult to quantify, like Meade 
(1977) implies, the situation can bring about misunderstanding. 
 
Intervention 
measures 
-LLINs, IRS, ACT 
 and Herbal medicine 
 
Figure 2. 4 Adapted from human ecology of malaria framework. (Source: Author, 
February 2017) 
 
 
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The left part of figure 2.4 shows behavioural and socioeconomic conditions. The behavioural 
determinants were derived from the ecological model. On the other side of the model are 
environmental factors related to the area to create favourable conditions for vector 
development. The built environment includes agricultural land use, settlement patterns, 
urbanization, while the physical environment involves rainfall, temperature, humidity, 
elevation and vegetation. The indictor in the middle of the model shows the population‟s 
relation with their ecosystem in one direction and the environmental conditions commanding 
those relations on the right side of the figure 2.4. Elevation, for instance, can affect a 
population‟s choice on where to construct a house. On the other hand, socioeconomic factors 
such as economic status may decide the type of house to construct. Culture manifestations 
about what causes the disease can influence one‟s access to health care as depicted on the left 
side of the figure 2.4. These factors relate in distinctive ways that can either strengthen or 
decrease the risk of malaria among populations open to similar risk factors. Based on how 
malaria spreads, intervention measures are put in place to help curb its spread. The 
intervention measures like IRS, LLINs, ACT and herbal medicine help in minimising the 
growth and spread of malaria parasites. 
The anticipated result of this model does at best diagnose a population‟s ecological factors 
related to the continuous elevated malaria incidence despite the best attempts created to 
prevent the disease. 
                            
 
 
 
 
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CHAPTER THREE:  STUDY AREA AND METHODOLOGY 
 
3.1 Introduction  
 
The study seeks to portray the geographic pattern of malaria cases in the Brong Ahafo Region 
using administrative districts as units of analysis. This section of the study includes 
geographic location, data, data source, research design, study population, sample and 
sampling procedure, research instrument, data collection procedure and method of data 
analysis in order to accomplish the objectives of the research. These techniques are based on 
information from literature and the research environment. 
3.2 Study Area 
 
The second biggest region of the ten regions of Ghana is the Brong Ahafo Region i.e. 16.6%, 
with a total land zone of 39,554 sq. km. The Region is bordered to the north by the Northern 
Region, on the south by the  Ashanti and the Western Regions, in the east by the Volta 
Region and in the southeast by the  Eastern Region and in the west by  La Côte d‟ lvoire. The 
Brong Ahafo Region lies in the west-central part of Ghana between 0⁰15‟ E-3⁰W as 
longitude and latitude of 8⁰45‟ N-7⁰ 30‟ (Ministry of Food and Agriculture, 2014).  
The capital of the Brong Ahafo Region is Sunyani. The region has 27 administrative districts. 
The average temperature in the Region is 23.9◦C and a relative humidity of 75% per year, 
with the highest humidity observed during the wet months. The rainfall pattern is either 
moderate or massive in the southern part of the region, while   the northern part registers a 
lower rainfall. The Region has two maximum rainfall trends, ranging from an average rainfall 
of 1400mm in the southern zone and 1000mm in the northern zone.  
The decreased level of rainfall leads to high temperatures in the region. The increase in 
temperature has a great influence on the rise of anopheles mosquitoes during the moderate 
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periods of rainfalls and this is reflected in the increase in malaria incidence in the region. In 
the Brong Ahafo Region the dominant vegetation zones are the rain forest belt reserves to the 
south and southwest. The northern part shows a transition to Guinea savanna woods (GHS, 
2006) 
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Figure 3.1 Location Map of the Study Area (Source: Author, 2017)                                                                                                                      
 
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3.3 Research Design 
 
The objective of this study was to examine the geographical distribution of malaria 
occurrence using cluster sampling and case study design. As stated by Bryman and Bell 
(2007) the case study design entails a comprehensive and intensified assessment of a sole or a 
handful of cases. The aim of the case study design is not to make a general statement about 
the conclusion, findings of different cases or of a sizable number of people, the interest lies 
on the cases and their diverse settings and to set a structural frame for deliberating on the 
development.  
The mixed methods research which is termed to be the third research paradigm was employed 
in this study. According to Fidel (2008) mixed methods research has not been frequently used 
by researchers in their recent publication. Greene (2006) has indicated that philosophical 
assumption of the mixed method needs to enlighten in social inquiry, debating that the 
philosophical assumption of ontology, epistemology, objectivity and subjectivity should be 
much enlighten.  The importance of the mixed methods research is indicated by Creswell and 
Plano Clark (2007), as the method with significant approaches to researchers in generating 
contradictory ideas and contested arguments in the research. The method gives researchers 
the idea to use any number of philosophical views while under the mixed method the research 
questions are the primary importance regardless of the philosophical worldview. For the 
purpose of this study, both qualitative and quantitative approaches (mixed methods) were 
used for data collection, analysis and presentation. 
 
Mixing both qualitative and quantitative approaches aided in establishing that the constraints 
of one kind of approach are balanced by the strength of the other approach. This is based on 
the established fact that understanding is obtained by mixing different approaches of 
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knowledge. The quantitative method was used to measure malaria risk relationships while the 
qualitative method was used to capture expressive information not showed in quantitative 
data about beliefs, feelings and motivation that underlie behaviours. It was used to uncover 
trends in thought and opinions and dive deeper into malaria disease. Using a mixed method, 
the technique has numerous merits and demerits. The method primarily displays its 
effectiveness and reduces the vulnerabilities of both techniques to enrich the whole research 
work, except that it involves greater effort. 
3.4 Study Population 
 
The 2010 population and housing census estimate the Brong Ahafo Region‟s population to be 
2,310,983, representing 9.4% of Ghana‟s population (GSS, 2010). There are 27 
administrative districts in the Brong Ahafo Region. Table 3.1 contains the complete list of the 
administrative districts of the region and their respective population. 
Table 3.1 showing 27 administrative districts with their population  
 
Districts Population 
Asunafo South 95,580 
Asunafo North 124,685 
Asutifi  105,843 
Dormaa Municipal 159,789 
Dormaa East 50,871 
Tano south 78,129 
Tano North 79,973 
Sunyani Municipal 123,224 
Sunyani East 85,272 
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Berekum 129,628 
Jaman South 92,649 
Jaman North 83,059 
Tain 108,386 
Wenchi Municipal 89,739 
Techiman Municipal 206,856 
Nkoranza South 100,929 
Nkoranza North 65,895 
Atebubu Amantin 105,938 
Sene 118,810 
Pru 129,248 
Kintampo South 81,000 
Kintampo North 95,480 
                   Source: Ghana statistical Service, 2010) 
 
3.5 Sample Size  
 
A simple equation can aid in the selection of a representative sample size. Computing for 
sample size, a few elements about the study population and sample needed must be 
determined. These are the total population of the study region, margin error, confidence level 
and the standard deviation. The sample for household(s) was accurately chosen from the 
health administration districts by applying cluster sampling probability proportional to size 
(PPS). The sample size was measured from 90% of people infected by malaria incidence 
from 2012 to 2016 in the Brong Ahafo Region, based on precision of 5% and 95% 
confidence interval. Utilizing the formula 
 
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npps =  deff x  Z²pq           (Source: Fisher et al.1991)                                                                          
          d²                                                        ……………….……………. (Equation 1) 
 
n = sample size population  
PPs = assuming proportion estimated precision absolute desired  
deff =  design effect  
d = precision absolute desired  
p = the combined (national) estimate  
q = proportional estimate  
Z = Z-value for a two-sided 95% confidence interval                                         
npps  =  2 x  1.96² x 90 x 8                                       
              5² 
          = 5531.905/25 
npps = 221.27616 
To have non-response and accept only, a 90% response rate of the sample was adjusted as 
below:  
n = 221.27616/0.9 = 245.9 
Formula for measuring the population to sample per cluster in a PPS survey  
 
Number to sample per cluster = npps  
                                                      m    
                                      245/11 =22............................................................. (Equation 2) 
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Where m is the number of cluster 
The sample size for the study was 245 units. The act of picking households, nurses and other 
health was to gain their views on malaria incidence and intervention. The sample size used 
for the study was 245 for questionnaire administration, and the sample shares were 
established upon the number of households in each cluster proportionately. 
 
Table 3.2 Sample Size by Clusters 
Districts(clusters) Sample share Households 
Berekum Municipal 13 2,227 
Sunyani Municipal 25 4,156 
Sunyani West 24 3,961 
Dormaa Municipal 6 1,384 
Dormaa East 19 3,121 
Jaman North 23 3,783 
Jaman South 19 3,134 
Tano South 24 4,004 
Techiman Municipal 17 2,849 
Wenchi Municipal 12 873 
Kintampo Municipal 63 2,010 
Total 245 39,818 
 
(Source: Author, 2017) 
 
 
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3.6 Sampling Procedure  
 
Multistage cluster sampling was utilized to choose respondents from households to whom the 
questionnaire was administered. The application of the multistage cluster sampling was 
divided into two (2) stages in this study. 
 In the first stage, in order to select equal proportions from each household, the ratio of units 
from target districts were selected. The target districts were eleven. The total number of 
households in the 11 districts was computed in Table 3.2. The eleven (11) districts were 
selected because they were districts with much available secondary data for the study. In 
order to determine the sample share for each district, the household ratio was divided by the 
total population multiplied by the sample size. 
In the second stage, random selection of household within the 11 clusters like household is 
chosen randomly within each cluster using the simple random sampling. The sampling 
interval is calculated as the number of households in the study area is divided by the number 
of cluster needed. The first house was randomly chosen and ascribed a number R, then the 
next house to be selected was the “R + ith” house. The interval was then added repeatedly 
until the desired sample was needed. 
3.7 Research Instruments 
 
Research instruments used to gather data included questionnaire and in-depth interview. 
3.7.1 Questionnaire Survey 
 
The questionnaire administration is the most important study tool that was used to gather 
household data. A total of 245 structured questionnaires with open and closed ended 
questions were administered in the study area. The quantitative data was used in the 
descriptive statistically analysis. The questionnaire contained questions pertaining to section 
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A which is the demographic characteristics of the study. The section B discussed questions 
relating to the spatial distribution of malaria incidence. The section C discussed questions 
pertaining to the correlation between malaria incidence and interventions. The section D 
discussed questions relating to interventions and their effectiveness.  (See appendix I) 
3.7.2 In-Depth Interviews  
 
An in-depth interview with malaria control policy makers at the district level was carried out. 
The research employed a semi structured interview approach for an in-depth interview. The 
interview guide contained issues on malaria distribution (incidence), control processes, action 
plans, results and problems of the malaria control process. The in-depth interview was 
employed to treat transcription and interpretation of data administered from the study area. It 
was used to ask questions in order to obtain insightful information regard perceptions, 
attitude and coping strategies.  
The in-depth interviews were conducted at Dormaa East, Jaman South, Sunyani 
Municipalities and at the national level of Malaria Control Programme in Accra. The in-depth 
interviews were conducted in these areas because the respondents were willing to work with 
and had time for the researcher. A total of two interviewees each were used at the above 
mentioned districts and two at national malaria control at Accra. The interviewees were 
selected purposively. 
3.8 Data Collection Procedure 
3.8.1 Map data 
 
GIS polygon map of the Brong Ahafo Region was created by the researcher. Data for creating 
the map was obtained from the University of Ghana, Geographic Information System (GIS) 
laboratory. 
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3.8.2 Secondary Sources of Data 
 
Secondary data used by this research were received from Ghana health service under the 
District Health Information Management System (DHIMS) and Policy Planning Monitoring 
and Evaluation (PPME). The emergence of DHIMS into the health institution has led to 
significance control of the challenges with data collection service. The data were collected 
from DHIMS on choosing districts of the Brong Ahafo Region. The results of microscopic 
diagnosis were confirmed from malaria OPD cases from 2012-2016. The population size of 
each district was obtained from the Population and Housing Census at the regional statistical 
service office at Sunyani. 
Climate data were acquired from Ghana Meteorological Agency at the regional office in 
Sunyani. This research concentrates on meteorological factors that are relevant to malaria 
distribution. Malaria incidence is the dependent variable, while rainfall, temperature and 
humidity are climatic independent variables. The rest of independent variables include 
elevation, vegetation, educational level, urbanization and economic status within the Brong 
Ahafo Region. 
3.9 Methods of Data Analysis 
3.9.1 GIS software 
 
GIS data were assembled and evaluated using the Arcmap 10.4 software [Environmental 
System Research Institute (ESRI)]. This software allows data concerning environmental and 
social features of a region to be mapped and examined close to areas and shape features of 
geographic attributes. 
3.9.2 Data analysis overview 
 
These spatial statistical tools were used to aid in understanding the geography of malaria in 
the Brong Ahafo Region because everything happens in space and time. These tools also deal 
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with the complex data sets which GIS deals with all the time. Getis and Ord Gi* statistics 
were used to ascertain the extent to which geographical distribution of malaria occurs in the 
Brong Ahafo Region. A Geographically Weighted Regression was employed to explain the 
relationship between malaria incidence and interventions. This aids identification of malaria 
control interventions and their effectiveness in the area. Ordinary Least Square was used for 
examination of the major factors in the malaria distribution in the study region.  
3.9.3 Getis and Ord Gi* statistic 
 
The    Getis and Ord Gi* statistic technique was used to measure hot spot features in the 
dataset. The results z-scores and p-values indicate where features with either high or low 
values cluster spatially. The high or low values of the spatial collection showed z-score and 
p-value of the outcome.  This technique assesses each attribute within the space of 
surrounding attributes. To have a hot spot that is statistically significant, there should be high 
value of the attribute with a high value of neighbouring attribute. The Getis and Ord Gi* test 
statistic is given by: 
        
 ……………… (Equation 
 ………………… 
3)                            .………………… (Equation 
4) 
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                                                                                    ..….………………. (Equation 5) 
Source: (Getis and Ord, 1992) 
Getis and Ord   Gi* statistics shows a significant positive z score; the greater the z score, the 
more concentrated the clustering of high values (hot spots). A significant negative z score, the 
lower the Z score, the more concentrated the clustering of low values (Cold spot). Local Getis 
and Ord Gi* Statistic are based on Z score (Getis and Ord, 1992). 
Clustering malaria incidence provides understanding about the spatial pattern of malaria 
incidence data as long as the malaria incidence is expected to be heterogeneous at every 
district level. In ArcGIS, the hotspot analysis was used to ascertain clusters, as malaria 
incidence was used as the input field per district from 2012 to 2016.  
 
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3.9.4 Ordinary Least Square 
 
Geographically Weighted Regression (GWR) is not a remedy for all regression difficulties as 
it is not considered as an instinctive choice in all regression modelling operations. The OLS is 
a default regression model found fit for this analysis.  
This part provides a general explanation of the evaluation process between malaria incidence 
and intervention in the Brong Ahafo Region. The likely appropriate variables for malaria 
forecasting were evaluated through the process of a collinearity analysis. The correlation 
between malaria incidence and its major causing factors is evaluated using the OLS. 
Collinearity refers to the correlation between independent variables. It normally happens 
when several independent variables are integrated in the regression model. The presence of 
collinearity influences its model evaluation and the explanation of the result(s). Collinearity 
does not influence the goodness-of-fit; it creates a situation in evaluating the specific results 
of each explanatory variable.  
The Variance Inflation Factor (VIF) analysis is seen as the result of collinearity. The VIF is 
defined as VIF = 1/ (1-R²j), where R²j= multiple relationship of the variable Xj. The VIF 
influences collinearity on the accuracy of values. The VIF indicates how collinearity makes 
the coefficient values likely to change (Freund & Littell, 2008). It shows how inflated the 
variance of the coefficient becomes and what might happen to a given variable when such a 
variable does not relate to other variables in the model. Collinearity becomes difficult to 
understand when the VIF exceeds the value of 10 which shows the VIF is greater than 1 
(one). 
The regression model was applied for the correlated reason of explaining and evaluating the 
relationship between the dependent (malaria incidence) and independent variables (LLINS, 
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IRS, antimalarial drug and herbal medicine) and achieved a precise prediction value of the 
dependent variable for future development.  A regression model equation is shown as  
           for i=1 … n  ……………………(Equation 6) 
In this linear equation  is the dependent variable (malaria incidence) and i is the location 
(study area) while Xi is the independent variables (LLINS, IRS, antimalarial drug and herbal 
medicine),   stands for the error,  and are elements which are to be evaluated to 
indicate t0hat the value is reduced to achieve n observation in the dataset. The term  is the 
fitted estimation for the “i” observations which predict “i” estimation for X. The formulae 
is considered as the residual for the “i” observation. The residual should be 
considered as autonomous and look exactly the same as the normal distribution with means of 
zero. Normally, the model is applicable to the process called the Ordinary Least Square. A 
multiple linear regression model equation is indicated as: 
          for i=1 … n  …………….(Equation 7) 
Where the forecast of the dependent variable is achieved through a linear integration of the 
independent variable. The OLS statistical function is 
                          ……………… (Equation 8) 
where stand for the vector of the evaluated parameter, X is the model that comprises  the 
value of the independent variables and its column, “y”, is the vector of observed estimations 
and is the other side of the variance and covariance design. 
The power of the model to repeat the observed y values is by calculation through the 
goodness of fit. This is properly shown by the R² value which operates from 0 to 1 as well as 
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calculates the correlation of variation in the observed y that is determined by the variation in 
the design. 
3.9.5 Geographically Weighted Regression  
 
The Geographically Weighted Regression (GWR) is a significant tool used to investigate 
geographic variables. The fundamental concept of the GWR is that elements can be measured 
at any place in the study area when assigned a dependent variable, and collection of 
independent variables which can be used to quantify an area whose position is determined. 
Interpreting Tobler‟s knowledge on proximity and correlation indicates that in evaluating 
parameters for a model at a specific area, the closest views should have the higher weight 
than distanced views. In the GWR, the dependent variable is y and the collection of the 
independent variables as Xk, k=1…m. For all the n views in the dataset to be quantified for its 
location is based on the correct coordinate system. The equation for GWR interpreting the 
OLS regression model is intended to be:    
                   ………(Equation 9) 
The term β0i (u) shows that the variable explains a correlation in the area u and is precise to 
such area. A declaration is made in advance for the dependent variable in the case that 
calculations for the independent variables are known at the area “u”. Generally, the areas 
where the variables were evaluated indicate where the dataset was acquired. The term “u” is 
used to indicate some general location in the study area.  Usually the term “u” stands for the 
vector of coordinates calculated in a projected coordinated system (universal transverse 
Mercator) or a geographic coordinate system (World Geodetic System 1984). In this study, 
the geographic coordinate system was used in measuring the “u”. 
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The calculation of the GWR is the same as that of Weight Least Square (WLS). The global 
model was stated previous only that the weights are put into a specific area „u‟ similar to 
other views in the data and therefore fluctuate for all areas. The formula has this format: 
                     ………………….. (Equation 10) 
Where  
         W (u) = square matrix of weights similar to location u  
          XTW(u) X = geographically weighted variance-covariance matrix  
           y= dependent variable  
The weights themselves are computed from a weighting scheme that is also known as a 
kernel. A number of kernels are possible: a typical one has a Gaussian shape: 
I. Kernel 
Kernel is the weighted blueprint for calculating weights. There are several kinds of the 
kernels, The Gaussian shape is considered as one of them and with this formula:  
                             ………………………. (Equation 11) 
Where wi(u) is the geographical weight of the i views in the data similar to the area u, di(u) is 
the distance between “i” views and the area “u” and “h” is a measure called the bandwidth. 
The distances between “i” views and the area “u” are usually Euclidean distances. The 
bandwidth in the kernel is given in a clear way as are the coordinates used in the dataset. As 
the weight of the bandwidth increases, the local GWR model gets closer to the global OLS 
model. 
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Other kernel types, except the Gaussian, can be applied in the GWR, despite the fact that in a 
pattern. It usually means really small, since the kernel type is similar to Gaussian. In relation 
to determining the goodness of fit of the model, the selection of a bandwidth is much more 
significant than the geometry of the kernel. A fixed bandwidth is used when data points are 
logical and regularly distanced in the study area 
In this study the adaptive bandwidth was used in determining the kernel type. The best way 
of achieving the adaptive bandwidth requirement is to select the kernel that permits similar 
data points for all measurements. The adaptive bandwidth is generally achieved by 
categorizing the distance of the data points from those of the study area. However, if the data 
points are not regularly distanced but clustered in the study area. It is usually worth having to 
permit the adaptive kernel to fit the unevenness, either by elevating the extent of the data 
points which are dispersed or reduce the extent of the data points which are compact.  
 
II. Bandwidth Method 
The Bandwidth method is made up of three methods in the GWR; these are the Akaike 
Information Criterion   (AICc), the Cross Validation (CV) and the BANDWIDTH 
COEFFICIENT. The AICc and CV are automatic methods that are applied to discover the 
bandwidth that delivers a good forecast. The bandwidth coefficient is based on the 
requirements of a bandwidth. The AICc method brings to light the bandwidth that reduces the 
AICc values, while the CV detects a bandwidth that reduces the CV score.  
The study made use of the AICc to determine the prediction where the AICc was used to 
measure the difference between the observed and the fitted values, as well as the error of the 
model. The error of the GWR model is determined not only by the number of variables used, 
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but also which bandwidth used as well. The relationship between the bandwidth and the error 
of the model is the logic for choosing AICc for the study. 
 
 
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CHAPTER FOUR: EXAMINING THE DISTRIBUTION OF MALARIA INCIDENCE 
4.1 Introduction 
 
In this chapter, the methods for data analysis are shown in conformity with the first objective 
of the study, which is the examination of malaria incidence distribution.  A malaria incidence 
map was confirmed with 245 points collected from the study area supported by malaria cases 
from 2012 to 2016. 
4.2 Perception of malaria incidence  
 
Table 4.1 shows that malaria is endemic in about 76% of the 245 study points or locations. 
Only 24% reported no malaria incidence in the last 4 weeks prior to the data collection. The 
study indicated that 76% of the respondents revealed that Brong Ahafo Region is an endemic 
area due to its internal geography which aids in the development of the mosquito.  
Table 4. 1 Perceptions of Malaria Incidence in the districts 
Perceptions about Malaria among Percentage (%)  
malaria incidence respondent  
Yes 185 76  
No 60 24  
Total 245 100  
(Source: Field Data, 2017) 
An informant from district malaria control office at Sunyani municipality revealed that 
“people in the community know that malaria is acquired through mosquito sting and also 
some people lack knowledge about the breeding sites suitable for mosquito such as solid 
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waste, wetlands, dry holes, with clear stagnant water and vegetation”. This is one reason that 
justifies why Brong Ahafo Region is highly endemic. 
Some respondents indicated that poverty is the cause of malaria and likewise malaria is the 
cause of poverty. It was further stated that due to poverty families are not able to provide 
good and nutritious foods as well as protective clothing and purchase mosquito nets and 
repellent sprays that could prevent a mosquito sting. 
4.3 Spatial patterns of malaria incidence 
 
The examination of the spatial pattern of malaria incidence indicated that Techiman and 
Sunyani Municipalities had the highest burden of 91% and 88% respectively. The reason for 
the high malaria incidence in Techiman Municipal, particularly, was a result of the unplanned 
settlement pattern in the district. This was indicated by 91% out of the 20 respondents in an 
interview. It was followed by Tano South (82%), Jaman North (70%), Sunyani West (67%) 
Wenchi (60%), Dormaa (55%), Jaman South (53%), Berekum (47%), Kintampo (41%) and 
Dormaa East Municipalities (40%). 
 
Table 4. 2 Spatial Patterns of Malaria Incidence in the Districts 
Cluster Yes No 
Percentage (%) Percentage (%) 
Sunyani Municipal 88 12 
Berekum Municipal 47 53 
Techiman 91 9 
Municipal 
Tano South  82 18 
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Dormaa Municipal 55 45 
Jaman South 53 47 
Kintampo North 41 59 
Jaman North 70 30 
Sunyani West 67 33 
Wenchi 60 40 
Dormaa East 40 60 
(Source: Health Data from Districts Health Information Management System, 2017) 
Some respondents revealed that malaria has become regular disease that is likely to happen 
with elevate levels of rainfall and rise in temperature. They believed that these major factors 
influencing malaria spread cannot be stopped. A male respondent from Techiman 
Municipality notes that “malaria is seen as regular disease nowadays and elevating unlike 
the past. People are no longer worried about malaria in the municipality due to death cases 
related to malaria. He further stated that malaria is a deathly disease due to the frequent 
death of people in the municipality”. Some respondents further added that malaria is regarded 
to be very common that when one dies, they feel another death case will be shortly heard of. 
This account indicated that malaria is a never ending story. 
4.3 Analysis of Hotspot Distribution of Malaria Incidence 
 
Getis and ord Gi* statistics are local indicators of spatial autocorrelation, which apply spatial 
neighbouring assumption to discover the clusters of geographic phenomena (Getis and Ord 
1992). Malaria data from 2012-2016 were used in the Getis and ord Gi* statistics analysis 
obtained from the study area.  
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Getis and ord Gi* statistically pinpoint the significant local clustering of high (hot spots) or 
low (cold spots) of malaria incidence in the Brong Ahafo Region. A high (hot spots) means 
positive  z score values (in brown colour) that confirm  areas with high malaria incidence, 
while a low (cold spots) represent negative z score values (in yellow colour) showing  areas 
with low malaria incidence. These maps depict clear spatial patterns of malaria incidence that 
were mostly spread in the western, eastern and south- eastern part of the Brong Ahafo Region 
from 2012 to 2016, while most of these areas are found to be urban or peri-urban. The 
statistically significant hot spots and cold spots of malaria incidence in the Brong Ahafo 
Region follow: 
Figure 4. 1 Malaria Hotspot 2012. (Source: Field Data, 2017) 
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Figure 4.1 shows hotspots in 2012, in the Sunyani and Techiman Municipalities and small 
sections of the Wenchi Municipality and Sunyani West Districts while the cold spots are in 
the Dormaa East, Berekum, Jaman North, Jaman South, Kintampo North. 
          
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 4. 2 Malaria Hotspot 2013. (Source: Field Data, 2017) 
 
Figure 4.2 portrays hotspots in 2013, the risk map created in such instances highlight areas of 
high risk that needs to be identified so as to tailor major interventions and monitoring 
activities. According to the map above, spatial distribution of malaria incidence is high in 
Techiman Municipal. The malaria incidence indicates cold spots in Dormaa East, Berekum, 
Jaman North, Jaman South, Kintampo North, Sunyani, Wenchi, Dormaa Municipalities and 
Sunyani West. 
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Figure 4. 3 Malaria Hotspot 2014. (Source: Field Data, 2017) 
 
In 2014, Sunyani and Techiman Municipality continued to exhibit a high malaria incidence. 
Jaman South, Jaman North and the Wenchi Municipality recorded higher malaria incidences 
than in the previous years (see figure 4.3). 
 
 
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Figure 4. 4 Malaria Hotspot 2015. (Source: Field Data, 2017) 
 
In 2015, Jaman South and Jaman North have become a malaria endemic area and indicated as 
a hotspot. The Techiman and Sunyani Municipalities maintained its high malaria incidence as 
a hotspot. There is some reduction in the case of the Sunyani West. 
 
 
 
                   
 
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Figure 4. 5 Malaria Hotspot 2016. (Source: Field Data, 2017) 
 
In 2016, Jaman South, Jaman North, Sunyani and Techiman Municipalities maintained their 
high malaria incidence shown as hot spots. Sunyani West had regained its previous hotspot 
indication. 
The Getis and ord Gi* statistics were computed for a yearly malaria incidence from the health 
data, producing z-score and p-value demonstrating the level of clustering. The spatial weight 
explains the neighbourhood search for each selected district and with the proximity of areas 
anticipated to have the same values. The observed values were correlated with the expected 
to show whether by chance the levels of malaria clustering were higher or lower than the 
expected values. The z score and p-values for each year present the probability that they were 
not produced randomly. A small p-value indicates that there is randomness in the spatial 
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pattern produced. The values of z score connected with Getis and ord Gi* statistic indicate 
that the concentration of a clustering increase  from z score of 9.3240 in 2012 to 12.0161 in 
2013, however, shows a reduction in clustering the years after. The highest of Getis and Ord 
Gi* statistic value was validated at 0.009 in 2013; while the lowest was examined in 2014 
(see Table 4.3). 
Table 4. 3 Analysis of high/low clustering for different years using Getis and Ord Gi* 
statistics 
Year Observed Expected Variance Z-score P-value 
 2012 0.00574 0.00320 0.000002 9.3240 <0.01 
2013 0.00963 0.00320 0.000017 12.0161 <0.01 
2014 0.00422 0.00320 0.00002 5.25119 <0.01 
2015 0.00523 0.00320 0.00004 6.76985 <0.01 
2016 0.00552 0.00320 0.00003 7.41459 <0.01 
 
(Source: Health Data from Districts Health Information Management System, 2017) 
The analysis of high/low clustering for different years as indicated in Table 4.3 demonstrates 
a connection with the maps above. The research indicates the spatial and temporal aspects of 
malaria disease in the Brong Ahafo Region, employing the GIS technique and a spatial 
statistical analysis, which permit for the calculation of the degree of clustering of malaria 
incidence. Such techniques have been employed to examine the spatial clustering of cholera 
causing factors in Kumasi, Ghana (Jerry, 2012) and malaria epidemiology in the Amansie 
West District (Larri, 2011).  
 
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4.4   Result of Major Factors Influencing the Pattern of Distribution 
 
Table 4.4 indicates OLS regression result of the major factors that the influence malaria 
incidence in the study area. The ordinary least square model, in this study was executed based 
on the formula below: 
Yi=Bo + B1Xi +B2Ui +B3Hi +B4Ai +B5Ti +B6Vi - B7Eai + B8Ebi - B9Zi - 
B10Ki………… (Equation 12) 
Where Y is the malaria incidence, X is rainfall, U is urbanization, H is humidity, A is 
agricultural land use, T is temperature, V is vegetation, Ea is educational level, Eb is 
elevation, E is economic status, and I is district subscript.  
Urbanization was shown to be a significant factor in malaria incidence in the Brong Ahafo 
Region. This result affirms with the result of Norri, (2004) which indicates that attaining 
knowledge about how the environmental variables effect the distribution of mosquitoes is 
significant in disease ecology. This is because the anthropogenic changes in climate and 
landscape (urbanization) are influencing the disease distribution. According to Johnson et al., 
(2008) and Steiger et al., (2012), environmental features influence mosquito spread widely 
since each mosquito vector has certain habitat demand which differ widely between 
connected vectors (Becker et al., 2010). 
 
 
 
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Table 4. 4 Summary of OLS result 
Variables Coefficient t-statistics 
Intercept 1022.562089 0.532609 
Rainfall 145.881628 1.260164 
Urbanisation 41.235771 0.046831* 
Humidity 56.135971 0.116851 
Agricultural land use 82.781629 0.918725 
Temperature 811.008119 1.0884293 
Vegetation -144.782509 -0.45850 
Educational level 305.827614 0.878824 
Elevation -213.341645 -1.370851 
Economic status -14.793022 1.729483 
 
(Source: Field Data, 2017) 
The study indicates that vegetation recorded negative coefficient as a factor of malaria 
incidence. This result differs from earlier findings which show that vegetation is positively 
linked with malaria incidence (Sharma, 1996; Mbooera & Kitua, 2001). The urban and peri-
urban nature is the probable reason in this specific case. Human settlements in urban areas 
show limited or no vegetation. Vegetation cover is mainly grouped as either natural or 
artificial habitats of the mosquito and shows that microclimate conditions make up 
temperature and humidity. These conditions create vegetation cover favourable for the 
mosquito to rest, breed and shelter and have the possible influence on the behaviour of the 
mosquito (Pavlovsky, 1966; Clements, 1999; Matthys et al., 2006a). 
Contrary to the respondents‟ expectation, temperature showed a positive correlation with 
malaria. Walsh et al. (1993) indicate that high temperature may elevate the progress at which 
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mosquitoes fully grow the rate of their blood feeding and the parasite obtained as well as the 
process of early development of the parasite within the mosquito. However, this is supported 
by data from the Brong Ahafo Region.  
The study showed a positive correlation between urbanisation and malaria incidence. The 
presence of solid and liquid waste generation and disposal created places for mosquitoes to 
breed. Environmental factor were the likely major factors behind the world intensification of 
malaria spread (Hay et al., 2004 and Gething et al., 2010). These factors usually happen at the 
same time as the socio-economic development and land use change (Bradley, 1996). Hay et 
al. (2005) and Tatem et al. (2008) state that urbanisation is one major factor of this 
development that causes malaria spread. 
Also, educational level, agricultural land use and humidity were positively related to malaria 
incidence at district levels in the Brong Ahafo Region. It is necessary for further studies to 
use panel data with more control variables to examine factors that may be correlated with 
malaria incidence in the Brong Ahafo Region. 
The model indicates that elevation has a negative relationship with malaria incidence in the 
study area. The null hypothesis stated that elevation was the main factor of malaria incidence 
distribution. However, the results, with a negative coefficient of (-213.341645), showed that 
elevation was not the main factor that influences malaria incidence distribution   This means 
there was a negative correlation between elevation and malaria incidence distribution in the 
Brong Ahafo Region. Hence the null hypothesis is rejected. 
The study demonstrates that there is negative relationship between malaria incidence and 
economic status. According to Sachs and Melany (2002), malaria is constantly considered as 
a disease of the poor. This proposition contradicts the conjecture that poverty threatens the 
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management strategies that could aid poor people to lessen their exposure to malaria (Yanda 
et al., 2006). 
 
 
 
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CHAPTER FIVE: CORRELATION BETWEEN MALARIA INCIDENCE 
 AND INTERVENTIONS 
5.1 Introduction 
 
The chapter assesses the correlation between malaria distribution and interventions used in 
the study area. The interventions for malaria control were provided by responses from 
respondents in the questionnaire as were interventions recommended by the NMCP. These 
interventions were LLINS, IRS and drug use. The correlation was analysed using the 
Geographically Weighted Regression (GWR). 
5.2 Malaria Control Interventions 
 
The malaria incidence data were obtained from the District Health Information Management 
System (DHIMS) and Policy Planning, Monitoring and Evaluation (PPME) of Ghana Health 
Services (GHS). The data were based on five years outpatient malaria incidence in the eleven 
districts of the study area.  
The data on interventions for malaria control came two fold from respondents in the 
questionnaire and from National Malaria Control Programme (NMCP). The interventions 
included LLINs, IRS, Herbal medicine and Drug Use. The impact of IRS for malaria control 
and prevention prior to the elimination period by WHO (1957), facilitated the entrance of IRS 
to be the most  important malaria control in history during the elimination period (Najera, 
1989) and currently considered as a significant instrument (WHO, 2012; RBM, 2008 and 
WHO, 2006). According to WHO (2005) LLINS intervention provides protection to people 
sleeping in order to avoid mosquito bites. This has decreased the ratio at which infected 
mosquitoes feed on the human population while sleeping or taking a rest as well as it 
decreases malaria spread (WHO, 2008). Early diagnosis and treatment is needed to decrease 
difficult incidences and mortality owing to malaria. The OLS regression indicated that the 
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four variables were used in the statistical evaluation. These variables were identified as the 
LLINs, IRS, anti-malarial drug and herbal medicine. The outcomes of the investigations for 
spatial autocorrelation and diagnostic for spatial dependence produced a clustered pattern of 
significance. The significance of the malaria distribution‟s   relationship with intervention is 
determined by the coefficient of the OLS.  
 
Malaria Control Intervention-OLS Model Results 
 
 
 …………………. (Equation 13) 
 
The results from Table 5.1 show that antimalarial drugs have negative relationships with 
malaria incidence.  The LLINs was shown to be a significant intervention for malaria control 
in the Brong Ahafo Region. It also showed that the IRS has a negative relationship with 
malaria incidence.  This contradicts that of Tukei et al.‟s (2017) study which indicates IRS 
has relationship with a significant reduction in malaria in Northern Uganda. Herbal medicine 
was negatively related to malaria control in the Brong Ahafo Region.  
With a VIF score of 2.22, collinearity was not an issue. The value of Jarque-Bera statistics 
show that the residual does not show normal distribution. However, the global Moran‟s index 
for spatial autocorrelation tests also indicate that model residuals were significantly biased, p 
< 0.004 
 
 
 
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Table 5. 1 Summary statistics for OLS 
Variables Coefficient t-statistics 
Intercept 2083.506460 0.000002* 
Antimalarial drug -59.243866 0.224189 
LLINS -95.862814 0.029797* 
Herbal Medicine -60.539122 0.098601 
IRS 55.094057 0.151189 
 
(Source: Field Data, 2017) 
Malaria = 2083.506₀-59.244X₁-95.862814X₂-60.539122X₃+55.094057X₄ ……… (Equation 
14) 
Where Xₒ=Y intercept 
             X₁= Anti-malarial drug 
             X₂= IRS 
             X₃= Herbal medicine 
             X₄= LLINS 
5.3 Malaria Control Interventions-GWR Results 
 
Test for GWR to examine whether the data as a whole shows the relationship between 
malaria distribution and interventions to control malaria against the null hypothesis that high 
malaria incidence districts are not likely to have many intervention programmes. 
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Here, interventions for malaria control have been identified to show the relationship with 
malaria distribution. At this point the question GWR will be permitted to answer is assessing 
the relationship between malaria distribution and intervention to control malaria across the 
study area. However, the OLS model was confirmed to show non-stationarity, hence the 
GWR is recommended to improve upon the OLS model. 
The GWR model in this study was executed based on the formula below: 
Malaria incidence(i)=Bio - B(i1)AMD(i) - B(i2)IRS(I )- B(i3)HM(i) + B(i4)LLINS(i) 
…..……. (Equation 15) 
Where B coefficient are interventions express as AMD is antimalarial drug, IRS is Insecticide 
Residual Spraying, HM is Herbal Medicine and LLINS is Long-Lasting Insecticide Nets. The 
GWR model with Adaptive Kernel and AICc Bandwidth was chosen to produce the AICc 
and the R² as indicated in Table 5.2. Correlating the fit of the OLS, which is the global model 
and GWR which is the local model, the result of the global OLS indicated that the adjusted 
R² was 0.05 with the analysis operated with 245 malaria incidence datasets. The adjusted R² 
for the local GWR was 0.22 with a bandwidth of 245 which shows that there has been further 
enhanced through the use of the local modelling technique. The recommended coefficient of 
the model fit AICc was 3859.877 for the OLS, while the local model, that is the GWR, 
produced 3818.811412. The GWR could interpret 88% of the total variation of the model. 
 
 
 
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Figure 5. 1 Map of IRS as malaria control intervention. (Source: Field Data, 2017) 
 
The geographic account of the local regression coefficient, (a) LLINS, (b) Herbal medicine, 
(c) Anti-malarial drugs, (d) IRS based on measurement of GWR model is depicted in  Figure 
5.1 and shows that the IRS regression coefficient was positive in Kintampo North, Sunyani  
and Techiman Municipalities while, negative in Dormaa East, Berekum, Jaman North, Jaman 
South, Wenchi Municipality,  and Dormaa Municipality 
 
 
 
 
 
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Figure 5. 2 Map of LLINs as malaria control intervention. (Source: Field Data, 2017) 
Figure 5.2 represents the regression coefficient of LLINs, which shows a positive coefficient 
in Kintampo North, Jaman North, Jaman South and Sunyani Municipality. Other districts, 
such as Dormaa East, Berekum, Tano south, Wenchi Municipality, Techiman Municipality 
and Dormaa Municipality had negative coefficients. 
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Figure 5. 3 Map of Herbal Medicine. (Source: Field Data, 2017) 
 
The herbal medicine coefficients as shown in Figure 5.3 were negative in most area districts 
in the Brong Ahafo Region. Higher coefficients of herbal coefficients were distributed in 
Jaman North, Jaman South, Sunyani and Techiman Municipalities. There were lower 
coefficients in Dormaa East, Berekum, Tano South, Sunyani West, Kintampo Municipality, 
Wenchi Municipality and Dormaa Municipality. 
 
 
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Figure 5. 4 Map of Anti-Malarial Drug as malaria control intervention.  
 
(Source: Field Data, 2017) 
 
Figure 5.4 indicates the validation of the non-stationarity of the Anti-malarial drugs 
coefficient. The map of the local coefficient of the Anti-malarial drugs showed that Jaman 
south, Techiman and Sunyani Municipalities had the higher coefficients. Lower coefficients 
were recorded by Dormaa East, Berekum, Jaman North, Sunyani West, Tano South, 
Kintampo North, Wenchi Municipal and Dormaa Municipality. The null hypothesis of this 
study is that high malaria incidence districts are not likely to have many intervention 
programmes. However, high incidence districts like Techiman Municipality, Sunyani 
Municipality, Sunyani West, Kintampo North, Jaman south and Jaman North had malaria 
control intervention programmes such as LLINS, Herbal medicine, Anti-malarial drugs and 
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IRS. Hence the null hypothesis that high malaria incidence districts are not likely to have 
many intervention programmes is rejected.  
This research gave an additional validation that there was a non-stationary correlation 
between malaria distribution and intervention for malaria control in the Brong Ahafo Region. 
It is evident that the Sunyani Municipality recorded a positive coefficient for all the 
interventions. Therefore, the findings showed that a geographic interplay between malaria 
distribution and interventions has an effect on malaria distribution.  
This thesis draws on the theoretical construct of human ecology of disease as initially argued 
as interplay between humans and their environment which leads to the spread or prevention 
of disease (Butterfield and Lewis, 2002). Hence, it is based on organization of malaria 
incidence in space which borders on the concept of human ecology of diseases. In this 
context, malaria is commonly regarded as urban disease (Klinkenberg et al., 2005). It is 
argued that urban dwellers have higher disposable incomes as well as better medical facilities 
and services (Deressa et al., 2007). However, in Africa urbanization has come the opposite 
due to incidence on urban poverty, urban blight and slums. Human interactions with the 
natural and built environment have generated wastes which are not properly managed leading 
to hygiene and sanitation problems. Malaria parasites take advantage of the conducive 
environmental conditions to multiply. The relationship which ensues between the urban 
human population and stinging mosquitoes that transmit malaria is a problem of this study.  
The international community has focused attention on malaria intervention in urban areas 
(WHO, 2012a). This warrants assessment of outcome of the targeted interventions on the 
incidence of malaria. Furthermore, the thesis considers the effectiveness of the interventions 
as another criteria area for investigation 
 
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CHAPTER SIX: MALARIA INTERVENTIONS EXISTING IN THE STUDY 
REGION AND THEIR EFFECTIVENESS. 
 
6.1 Introduction 
 
The theme of the last aspect of the analytical chapter relates to the fourth objective of the 
study, which deals with the malaria interventions existing in the districts and their 
effectiveness. The analysis in this section is based on the use of tables and charts to show 
variation between variables. 
6.2 Malaria Control Interventions in the Study Region  
 
Distinctive approaches to malaria control interventions have been in place in the Brong 
Ahafo Region to control the disease. These approaches for malaria control have regularly 
been changed through time as ways to enhance their effectiveness. The respondents 
mentioned that there are several interventions through which malaria can be controlled, 
however, emphasis was laid mostly on LLINS, IRS, anti-malarial drugs and herbal medicine 
in the study region. Much emphasis was laid on above mention interventions because they 
were interventions commonly used in the study area. 
6.3 Indoor Residual Spraying (IRS)  
 
Indoor residual spraying is a highly effective malaria control intervention which involves 
spraying the interior walls of the house using insecticides to kill the mosquito vectors (WHO, 
2009).  The WHO advocates that spraying of mosquitoes should be done at least once each 
year in endemic areas as a way to reduce mosquitoes. The spraying is mostly done using 
chemical such Dichloro-Diphenyl-Trichloroethane (DDT). The IRS kills mosquitoes usually 
when the vector has had a blood meal and is taking a rest indoors. This signifies that the IRS 
can only have an effect on the mosquito vector after an infectious bite, but not before. Hence, 
the IRS is not an effective way to control malaria transmission (NetMark USAID, 2007). 
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6.3.1 Effectiveness of Indoor Residual Spraying (IRS) as an Intervention in the Study 
Region 
 
The result of the research showed that the utilization of IRS as an intervention in controlling 
malaria incidence in the study area was generally less significant, however, the intervention 
was supposed to produce good results without costing a lot of money. The findings showed 
that just 18% of the household were discovered to spray their house sometimes. 
The reason for low usage of the IRS was attributed to the high cost of the residual sprays. It 
was indicated 82% of the respondent had not embraced IRS. However, the respondents 
conceived it was a fruitful intervention for controlling and preventing malaria incidence.  The 
IRS is a good control intervention for protection of areas such a refugee camp, migrants and 
military personnel (WHO, 2013). The National Policy for Vector Control by National 
Malaria Control Programme (NMCP) in April 2007 included IRS as a component of malaria 
control intervention. The IRS was introduced to the account of the effective implementation 
process at Obuasi in the Ashanti region of Ghana in 2005. The implementation process led to 
an intense reduction of malaria incidence from 7000 to 1000 reported OPD cases annually 
(NMCP Annual Report, 2008).   
 The respondents were asked to rank the effectiveness of the IRS based on these statements: 
Very effective, Effective, Not effective and Undecided and their opinion is shown in the 
chart. Figure 6.2 shows that the IRS was “Not effective” in the study region. 
 
 
 
 
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Figure 6.1 Respondents’ perception about effectiveness about IRS (Source: Field Report 
2017) 
6.4 Long Lasting Insecticide Nets (LLINs) 
 
The LLINs is one of the effective interventions of controlling malaria in the Brong Ahafo 
region. In 2010, the NMCP and other development partners introduced the „catch-up‟ 
approach of carrying out the free mass distribution campaign with the aims of accomplishing 
universal coverage of LLINs in all the regions of Ghana by 2012 (NMCP, 2010). 
The LLINs have the capability to prevent frequent bites of mosquito provided that they are 
correctly used (WHO, 2005). In 2012, mass distribution of LLINs was done in the Brong 
Ahafo Region and total of 1,457,399 nets were distributed in the region (NMCP, 2015). Each 
bed net was to accommodate an average of 1.8 persons which means at least two people. This 
indicates that the mass distribution of LLINs to households may not be adequate to fight 
malaria occurrence (WHO, 2004). 
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6.4.1 Possession of LLINs as a Malaria Control Intervention  
 
The results in the ownership of bed net showed 84% of the respondents owned some kind of 
a bed net. In spite of the high percentage of household ownership of bed net, a remarkable 
number of households (16%) do not have any kind of bed net (see Table 6.1). The difference 
between those who owned a bed net and those who do not was a single aspect that weakened 
the effectiveness of malaria intervention in the study region. It was revealed from the results, 
that the LLINs were the common bed nets possessed by many respondents. One of the 
National Malaria Control Programme informants in Sunyani Municipal stated that “LLINs is 
an effective way to curb malaria transmission in endemic areas and that the LLINs should be 
considered a public good for all people living in malaria endemic areas”. 
Table 6. 1 Possession of Bed nets and Malaria incidence 
Bed net Possession and Frequency Percentage (%) 
Malaria Prevalence 
 (N=245) 
Yes 205 84 
No 40 16 
Total 245 100 
(Source: Field Data, 2017) 
6.4.2 Utilization of Long-Lasting Insecticide Net (LLINs)  
 
Based on the findings, it was revealed that in spite of the report about LLINs, not every 
household had acquired LLINs. The LLINs accommodate at least two individuals. Hence, 
some household members are not accommodated under LLINs. A National Malaria Control 
Programme informant stated that “our reports indicate that LLINs is used by at least two 
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persons in a household.  Most households have more than two members. Hence, we conclude 
that there are other household members not covered by LLINs”. 
The respondents were requested to rank the effectiveness of the LLINs after assessing the use 
of the LLINs. The effectiveness of the LLINs was ranked based on these statements: very 
effective, effective, not effective and undecided as shown in Figure 6.2.  
 
 
 
 
Figure 6. 2 Respondents perception about effectiveness of LLINs (Source: Field Report 
2017) 
 
6.5 Antimalarial drugs as malaria control intervention 
 
There are different drugs which are mostly used in treating malaria. In most cases, these 
drugs are prescribed to be taken on daily basis by those infected with malaria disease. In 
endemic areas like the Brong Ahafo Region, prophylactic drugs are used full-time by people 
infected with malaria because such people have some level of immunity to fight the parasites. 
The use of prophylactic drugs is normally restrained/confined to children under five years, 
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pregnant women and travellers to malaria endemic regions, since these people/categories 
have little or no immunity to fight the malaria parasite (WHO, 2009). 
The use of quinine as a prophylactic to fight malaria dates back to the seventeenth century. 
Quinine is still used today in treating chloroquine resistant Plasmodium falciparum. In the 
twentieth century, there have been other effective prophylactic drugs such as quinacrine, 
chloroquine, and primaquine to reduce usage of quinine (Toovey, 2004). 
Currently there have been modern drugs such as mefloquine (Lariam) (Jacquerioz, 2009), and 
the combination of atovaquone and proguanil hydrochloride (Malarone) in treating malaria. 
One‟s choice of drug in the study region is based on drugs that can kill the parasite and its 
side-effects.  
6.5.1 Effectiveness of Antimalarial Drugs as an Implemented Intervention  
 
The research also showed that both private and public health providers in the study area 
administered antimalarial drugs. It was indicated that three different types of antimalarial 
drugs were mostly administered in both private and public clinics in the study. These 
antimalarial drugs were ACT, Quinine and Amodiaquine. 
The study indicated that 76% of the respondents had accepted ACT as an effective malaria 
control. In 2005, the Ministry of Health, Ghana established series of actions to achieve the 
result of utilizing ACT based on WHO recommendations (GHS, 2004). It was revealed that 
quinine represented 20%, while Amodiaquine represented 4%, which showed that 
respondents had not embraced quinine and Amodiaquine in the study region. Amodiaquine 
was first recommended for uncomplicated malaria incidences in any country suffering from 
resistance to monotherapy (GHS, 2004). Figure 6.3 shows views on effectiveness of each 
antimalarial drug in the study area. 
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4
20
ACT
Quinine
76
Amodiaquine
 
Figure 6. 3 Respondents’ perception about effectiveness of antimalarial drugs (Source: 
Field Data, 2017) 
 
6.6 Herbal medicine as malaria control intervention 
 
As malaria remains one of the deadly disease and its resistance to modern medicine is 
causing people to feel danger. However, the poor cannot afford the modern medicine or lack 
access to modern medical facilities. Hence, herbal medicine could be the significant way to 
control malaria. Lucas (2010, p. 76) indicates that herbal medicine is „the use of plant 
products to treat or prevent a disease‟. There are different types of herbs that are used for 
medicinal purposes. These may include leaves, flowers, stems, roots, seeds, and berries 
(Woolf, 2003).  
Herbal medicine is affordable and accessible, as they are made from local plants cut from the 
forest or in homes. In Africa, for primary health care, about 80% of the population applies 
herb medicine (WHO, 2002a; Mensah, 2008). Herbal medicine is used in treating 60% of 
children with high fever emanating from malaria in Ghana, Mali, Nigeria and Zambia (Darko, 
2009; Okigbo & Mmeka, 2006; WHO, 2003a; WHO, 2003b; Abbiw et al., 2002).  
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6.6.1 Effectiveness of Herbal Medicine as Intervention Implemented in the Study Area 
 
The findings of the research indicated that the use of herbal medicine as an intervention in 
controlling malaria in the study region was generally significant. 
A key informant from Dormaa East district stated that “despite the fact that herbs used by 
local people are likely to be effective in controlling malaria, the herbs are likely to result in 
other health effect. It was further indicated by some respondents in the Dormaa East District 
that an herb like neem tree (Azadirachta Indica) has the ability to control pests in the farm. 
Hence, the possible damage to human life, particularly, when taken for a long period to treat 
malaria should not be underestimated”. In Ghana, for primary health care, about 70% to 75% 
of the people use herb medicine (WHO, 2001; Abbiw et al., 2002). Yeboah (2000) indicates 
that traditional health services cannot solve all health situations in Ghana. While some herbs 
may be regarded as healthy, they may have critical side effects (IUPAC, 2008). 
Respondents revealed that the location is one element which affects the choice of herbal 
medicine in controlling malaria in the study area. It was indicated that people staying in urban 
areas normally prefer using the orthodox means of intervention (LLINs, IRS and anti-
malarial drugs). People living in rural areas in the same district prefer the traditional means of 
intervention such as herbal medicine. Respondents living in rural areas of the study region 
also stated that they use the orthodox means of intervention (LLINs, IRS and antimalarial 
drugs), however, they still drink herbal medicine to aid their body get rid of the malaria 
parasites.  
The effectiveness of the herbal medicine was ranked based on these statements: Very 
effective, Effective, Not Effective and Undecided. Figure 6.4 show that the usage, herbal 
medicine was Effective in the study region. 
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Figure 6. 4 Respondents’ perception about effective of herbal medicine (Source: Field 
Data, 2017) 
 
6.7 Interventions Targeting Malaria Incidence  
 
The research established that 79.2% of the respondents understood that malaria distribution in 
the study region can be controlled despite other respondents had other thoughts. However, the 
other respondents (20.8%) assumed that clearing of the vegetation and draining of gutters was 
the greatest interventions of controlling malaria incidence. Moreover, the use of LLINs, IRS, 
anti-malaria drugs and herbal medicine were interventions that were significantly noted. The 
respondents stated that the LLINs was the ultimate intervention of control malaria incidence.  
According to Lipowsky et al., (1992), another school of thought have tried to establish the 
key factors of effective malaria control. It was indicated that understanding human 
perceptions of malaria and factors that affect these perception should be the key way of 
finding a significant intervention for malaria control throughout the world. Several factors do 
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influence the treatment seeking behaviour among malaria patients.  Asenso-Okyere et al., 
(1997a), indicated that the cost of malaria treatment has a direct and indirect effect on 
household. The direct cost stated as the cost of mediation and transport while the indirect cost 
becomes the time wasted. 
6.8 Challenges of the Interventions for Malaria Control 
Possible challenges of effective malaria control interventions from the respondents were poor 
sanitation, inadequate basic information on malaria, fake drugs, high cost of anti-malaria 
drugs and herbal medicine, low rate of LLINs and other bed nets as well as fake medical 
practitioners. Likewise, the respondents added that the bad attitude of individuals on many 
occasions failing to complete a full dosage of malaria drugs can suddenly increase the 
resistance of the parasite to the anti-malaria drugs.  
A key informant from NMCP in the Jaman North district stated that “insufficient funds 
influence the entire implementation plans for malaria control interventions as well as affect 
their means to increase the existing interventions appropriately”.   The official at NMCP, 
particularly pointed out that “we have best action plans to control malaria however we are 
not capable to rise adequate funds to increase the LLINS distribution and completely 
implement the IRS”. The officials of NMCP at the district level also revealed that malaria 
control is combined with the Primary Healthcare (PHC) in Ghana. However, the officials 
stated that malaria control has not fully operated well in association with PHC which has led 
to an unproductive workforce at the local level. The health sector in malaria endemic areas is 
usually charged with malaria control intervention as a way of measuring the control (WHO, 
2008 However, there are numerous conditions why malaria still remains a key health problem 
(http: malariajournal.com, 2008). According to the World Malaria Report (2008), the 
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conditions of not being able to convince policy maker is a key challenge for vector control 
intervention. 
6.9 Coping Strategies to Manage Malaria 
 
Coping strategies are described as blueprints used by households to reduce the cause of a 
problem. As a result of the high malaria incidence indicated in the study region, respondents 
were required to provide their coping techniques to help control malaria. 
Some respondents at Techiman Municipality stated that “wearing long slit and a pair of 
trousers to avoid bites at night especially, closing windows and doors, burning of orange 
peels, and clearing of the environment were the techniques they have developed on their own 
to curb malaria incidence”. Other respondents also indicated that the Community-Based 
Organisations (CBOs), National Sanitation Day (NSD) and NGOs act as agency that aid in 
the cleaning of the environment as well as giving health education to respondents. 
 
 
 
 
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CHAPTER SEVEN: SUMMARY, CONCLUSION AND RECOMMENDATIONS 
 
7.1 Introduction 
 
This chapter draws conclusions on the geographic account of malaria incidence and 
intervention in the Brong Ahafo Region. It consists of summaries of findings based on the 
key analytical themes, thus providing answers to the research questions. Both the conclusion 
and the recommendations address identified problems. 
 
7.2 Summary of the Major Findings 
 
This section of the research provides a summary of the major findings with reference to the 
objectives outline for the study. 
I. Spatial distribution of Malaria Incidence; Following what other scholars have 
established about malaria incidence in malaria endemic areas, the thesis 
established that the Brong Ahafo Region had a high malaria incidence. The 
research had 76% of the respondents who revealed that their districts were malaria 
endemic as a result of poor sanitation. However, 76% of the respondents added 
that they have had malaria in the past 4 weeks, while 24% of the respondents had 
not reported to be sick during the same period. The demand to examine the spatial 
distribution of malaria incidence in the study area stimulated the use of hotspot 
analyses (Getis and Ord Gi* statistics) using malaria incidence on a yearly basis 
as the input field. The output of the hot spot analyses produced hot spot maps of 
malaria incidence from 2012-2016 which indicated clusters in the study area with 
hot zones noted as significant clusters of high values being positive and cold 
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zones as significant clusters of low values being negative. Figure 4.1 shows 
hotspots in 2012, in the Sunyani and Techiman Municipalities and small sections 
of the Wenchi Municipality and Sunyani West Districts while the cold spots are in 
the Dormaa East, Berekum, Jaman North, Jaman South, Kintampo North. Figure 
4.2 portrays hotspots in 2013, in Techiman Municipal and in some tracts in 
Sunyani and in the Wenchi Municipalities. Dormaa East, Berekum, Jaman North, 
Jaman South, Kintampo North, Dormaa Municipality and Sunyani West indicate 
cold spots. In 2014, Sunyani and Techiman Municipality continued to exhibit a 
high malaria incidence. Jaman South, Jaman North and the Wenchi Municipality 
recorded higher malaria incidences than in the previous years (See figure 4.3). In 
2015, Jaman South and Jaman North have become a malaria endemic area and 
indicated as a hotspot. The Techiman and Sunyani Municipalities maintained its 
high malaria incidence as a hotspot. There is some reduction in the case of the 
Sunyani West (See figure 4.4). In 2016, Jaman South, Jaman North, Sunyani and 
Techiman Municipalities maintained their high malaria incidence shown as hot 
spots. Sunyani West had regained its previous hotspot indication (See figure 4.5).   
II. The regression model was applied for the correlated reason of explaining and 
evaluating the relationship between the dependent (malaria incidence) and 
independent variables (rainfall, temperature, humidity, elevation, vegetation 
educational level, economic status, agricultural land use and urbanization). The 
study indicated that urbanization was the likely factor to cause malaria incidence 
based on the strength of the coefficient. The model demonstrated that there was no 
relationship between elevation and malaria incidence which means that elevation 
was not a major factor influencing malaria incidence distribution in the study area. 
The null hypothesis stated that elevation was the main factor of malaria incidence 
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distribution. However, the result showed that elevation was not the main factor 
that influences malaria incidence distribution. The null hypothesis was rejected.  
III. The interventions for malaria control were provided by the responses from the 
questionnaire as well as interventions recommended by the NMCP. These 
interventions were Long Last Insecticide Nets (LLINs), Insecticide Residual 
Spraying (IRS) and Drug Use. The correlation was analysed using the 
Geographically Weighted Regression (GWR). The malaria incidence data were 
obtained from the National Malaria Control Programme (NMCP) of Ghana Health 
Services (GHS). The data were based on the five years‟ outpatient malaria 
incidence in the eleven districts of the study area.  A thin line is drawn between 
OLS and GWR in assessing the relationship between the malaria and 
interventions. The Geographically Weighted Regression (GWR) is not a remedy 
for all regression difficulties nor considered   instinctively as the preferable choice 
in all regression modelling operations. The OLS is a default regression model 
found fit for this analysis. A negative coefficient states that there is an inverse 
relationship between   distribution and interventions while a positive coefficient 
indicates that a relationship exists between the malaria distribution and 
intervention. The result indicated that anti-malarial drugs, IRS and herbal 
medicine recorded low negative coefficients with t-statistics greater than 0.05 
which imply that anti-malarial drug, IRS and herbal medicine as interventions had 
a negative relationship in controlling malaria distribution. The LLINs was the only 
intervention that recorded a positive coefficient with t-statistic less 0.05 showing 
that LLINs has a direct relationship with malaria distribution in the study area. 
Therefore, the OLS model was confirmed to be non-stationary, hence the GWR is 
recommended to improve upon the OLS model. The GWR regression model 
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showed that LLINs had correlation with malaria incidence in more than two 
districts which indicates that the intervention was positive. Antimalarial drugs and 
IRS were significant in Kintampo North, Jaman South, Techiman and Sunyani 
Municipalities. The herbal medicine coefficients were negative in most districts in 
the study region except in Jaman South, Jaman North, Techiman and Sunyani 
Municipalities. This asked for a rejection of the null hypothesis that high malaria 
incidence districts are not likely to have many intervention programmes rather 
than high malaria incidence districts are likely to have many intervention 
programmes.  
IV. There is some evidence to support a view that interventions implemented have been 
effective in controlling malaria in the study region. Many of the respondents 
stated the LLINS was the ultimate intervention for controlling malaria incidence. 
Further interviews with the officials of the National Malaria Control Programme 
in Accra indicated that the LLINS is an effective way to curb malaria transmission 
in  endemic areas and that the LLINS should be considered a public good for all 
people living in malaria endemic areas. The results of the research showed that the 
utilization of IRS as an intervention in controlling the malaria incidence in the 
study area was generally less significant. The reason for low usage of the IRS was 
attributed to the high cost of the residual sprays. The research also demonstrated 
that both private and public health providers in the study area administered anti-
malarial drugs. It was indicated that three different types of antimalarial drugs 
were mostly administered in both private and public clinics. These anti-malarial 
drugs were ACTs, Quinine and Amodiaquine. The findings of the research 
indicated that the use of herbal medicine as an intervention in controlling malaria 
in the study region was generally significant. Some respondents believed that even 
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though herbs used by the local people are likely to be effective in controlling 
malaria, the herbs are likely to result in other health problems. It was further 
indicated by respondents that on the assumption that a particular herb like neem 
tree has the ability to control insects on farmlands then the herbs should not be 
discounted to have an adverse effect, particularly on human health, when taken for 
a long period as malaria medication. The respondents indicated that possible 
challenges of effective malaria control interventions were poor sanitation, 
inadequate basic information on malaria, fake drugs, high cost of antimalarial 
drugs, low rate of LLINS and the use of other bed nets   among households and 
fake medical practitioners. The respondents revealed their coping strategies 
against malaria. These were the wearing of long slits and pairs of trousers to avoid 
bites at night, especially, closing windows and doors, burning of orange peel, and 
clearing of the environment of weeds and waste. 
7.3 Conclusion 
 
This study sets out to examine the spatial distribution of malaria incidence in the Brong 
Ahafo Region by using a Geographic Information System (GIS) as an analytical tool. The 
result of this research indicates that malaria incidence in the Brong Ahafo Region 
demonstrated a spatial pattern not only on some meteorological variables, but also on 
socioeconomic factors such as agricultural land use and urbanisation. The OLS was 
performed under the null hypothesis that elevation has a negative relationship with the 
malaria incidence in the study area. Hence the null hypothesis was rejected. 
The incidences of malaria in the study region were significantly clustered showing hotspot in 
2012, the Sunyani and Techiman Municipalities and small sections of the Wenchi 
Municipality and Sunyani West Districts while the cold spots are in the Dormaa East, 
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Berekum, Jaman North, Jaman South, Kintampo North. It also portrayed hotspots in 2013, in 
Techiman Municipal and in some tracts in Sunyani and in the Wenchi Municipalities. 
Dormaa East, Berekum, Jaman North, Jaman South, Kintampo North, Dormaa Municipality 
and Sunyani West indicate cold spots. In 2014, Sunyani and Techiman Municipality 
continued to show high malaria incidence. Jaman South, Jaman North and the Wenchi 
Municipality recorded higher malaria incidences than in the previous years. In 2015, Jaman 
South and Jaman North have become a malaria endemic area and indicated as a hot spot. In 
2016, Jaman South, Jaman North, Sunyani and Techiman Municipalities maintained their 
high malaria incidence shown as hot spots.  
The correlation between malaria incidence and interventions were analysed using GWR. The 
control interventions included LLINs, IRS, ACT and herbal medicine. The analysis showed 
that LLINs had correlation with malaria incidence in more than two districts which indicates 
that the intervention was positive. Antimalarial drugs and IRS were significant in Kintampo 
North, Jaman South, Techiman and Sunyani Municipalities. The herbal medicine coefficients 
were negative in most districts in the study region except in Jaman South, Jaman North, 
Techiman and Sunyani Municipalities. The GWR was performed under the null hypothesis 
that high malaria incidence districts are not likely to have many intervention programmes. 
Hence the null hypothesis was rejected because high malaria incidence districts have many 
intervention programmes like LLINS, IRS, Herbal medicine and Antimalarial drugs. 
The result of malaria control interventions and their effectiveness indicated that LLINS was 
the only malaria intervention that had a positive coefficient because it was revealed that 
LLINS was the effective way to curb malaria transmission in endemic area like Brong Ahafo 
Region. However, interventions like antimalarial drugs, herbal medicine and IRS recorded 
low negative coefficient due to cost of the residual sprays and antimalarial drugs. It also 
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indicated that the location is one element that affects one choice for herbal medicine in 
controlling malaria. 
 7.4 Recommendations 
 
The study findings revealed that there were hotspot zones in the study area. In order to reduce 
the negative effects that follow, the following measures should be taken into consideration.  
 Malaria clusters were detected in the study area and the single difference between the 
cold, not significant and hot spots were analysed. There is the need for further study 
to analyse the causes underlying each of the detected hotspots in order to identify 
special intervention measures. Therefore, the detected locations of clustered areas 
whose lives are at risk of malaria need more attention from the government of Ghana 
and other organizations. 
 The result of this study demonstrates that lack of difference between malaria protected 
and unprotected people. There is a need to further study on human education and 
behaviour in malaria to assess the underlying factors because they might influence the 
use of malaria control measures. Therefore, the WHO and other organizations is 
needed to address the problem of malaria protected and unprotected people who 
deserves well-tailored information education and communication. 
 The continuity of effectiveness of malaria interventions in controlling malaria, 
especially in a hot spot, needs vigilant monitoring of the National Malaria Control 
Programme in Ghana. The government of Ghana needs to give the due attention to the 
poor, distant and inaccessible households in the effort of malaria intervention 
programmes such as LLINs distribution. 
  
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                                             APPENDIX I: QUESTIONNAIRE 
 
                                UNIVERSITY OF GHANA, LEGON 
DEPARTMENT OF GEOGRAPHY AND RESOURCE DEVELOPMENT 
RESEARCH CONDUCTED ON GEOGRAPHIC ACCOUNT OF MALARIA 
INCIDENCE AND INTERVENTIONS IN THE BRONG AHAFO REGION, A GIS 
APPROACH. I  AM SEEKING FOR YOUR ASSIST TO ACHIEVE THIS GOAL. 
THIS IS PURELY AN ACADEMIC WORK AND YOU ARE FULLY ASSURED OF 
THE CONFIDENTIALITY OF YOUR RESPONSES. THANK YOU  
Do you have a preference for written or verbal consent? (Mark choice, signature required for 
written consent)    
1. Written consent……………………….                2. Verbal consent………………………..  
Part A: cluster code…………………     Location: GPS code…………………………..  
Please thick or indicate the correct response.   
Section A: Socio-Demographic Characteristics of Respondents   
1. Gender of respondent (1) Male [  ] (2) Female [  ]   
2. Age of respondent……………   
3. Level of education…………………..  
4. Occupation……………………………… 
 
 
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Section B: Spatial distribution of malaria incidence 
1. At what month of the year do you or members of your household experience malaria most 
frequently? Please rank them 
January            
February     
March  
April  
May    
June     
July     
August     
September  
October  
November     
December  
 
2. List 10 things in the level of importance that causes malaria in your community? 
1……………………..                                      6……………………… 
2……………………..                                       7……………………... 
3………………………                                     8……………………… 
4………………………                                      9……………………. 
5………………………                                     10…………………….                                           
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3. How often do people get malaria in your family within a year?  
(1) 2 weeks      (2) 4 weeks   (3) 4 months   (4) 8 months 
 
4. How often do people get malaria in your community?  
………………………………………………………………………… 
5. In your view will you consider your community to be malaria endemic? 
(1) Yes                        (2) no 
If yes why? 
………………………………………………………………………….. 
If No, why? 
…………………………………………………………………………. 
6. Which of these factors below will you consider to be most important contributor of malaria 
in your community? Please rank them. 
General environmental factors Rank 
Temperature  
Humidity   
Elevation  
Rainfall  
Vegetation  
 
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7. Which of these factors below will you consider to be most important contributor of malaria 
in your community? Please rank them. 
Human factors Rank 
Urbanization  
Educational level  
Agricultural land use  
Economic status  
 
 8. Rank the month or months you notice a lot of mosquitoes in your area?                                                                              
Months Rank 
January            
February     
March  
April  
May    
June     
July     
August     
September  
October  
November     
December  
                                           
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Section C: Correlation of malaria incidence and intervention 
 9. What malaria interventions programmes and methods are been implemented in your 
community? 
Interventions Method used 
  
  
  
  
  
  
 
10. How will you grade the performance of the intervention for malaria control? 
interventions Excellent Very Good Good Average Poor 
      
      
      
      
      
      
 
11. Who implemented the interventions? 
……………………………………………………………………… 
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12. How long has the interventions been implemented? 
……………………………………………………………………… 
13. Have the implemented interventions reduced the occurrence of malaria in your area?  
Yes [   ]          No [    ] 
14. How do you rank the effectiveness of the interventions in your community?  
1. Very strong [  ]       2. Strong   [   ]          3. Not strong [    ]                  4. Undecided [   ] 
15. Do areas with high malaria incidence have many interventions programmes?  
Yes [  ]     no [   ]  
(a) If yes, state them 
………………………………………………………………………………. 
………………………………………………………………………………. 
………………………………………………………………………………. 
     (b) If no, why 
           ……………………………………………………………………………… 
           ……………………………………………………………………………… 
          ……………………………………………………………………………… 
16. Are people in areas of high malaria incidence receiving the interventions in your 
community?  
Yes [   ]          No [  ]  
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17. Do you do indoor residual spraying?  
Yes [   ]          No [   ] 
Section D: Interventions and their effectiveness 
18. If you do rank the effectiveness of indoor in the control of malaria?                 
1. Very strong [  ]    2. Strong [  ]    3. Not strong [  ]    4. Undecided [   ]  
19. Who sprayed the house?  
1. Government worker/program [  ]   
2. Private company [   ]  
 3. Household member [   ]  
 4. Other, specify………………  
 5. Don„t know [  ]   
20. Do you use bed net in your community?  
 Yes [  ]        No [  ] 
21. If you do rank the effectiveness of bed net in the control of malaria?  
1. Very strong [  ]    2. Strong [  ]    3.Not strong [  ]    4. Undecided [   ]  
22. What type of net do you use or own?  
1. ITN [  ]   2. LLIN [  ]   3.Untreated net [  ]    4. Don„t know [  ]  
 23. What coping strategies have you adopted on your own to manage malaria?  
………………………………………………………………………………… 
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………………………………………………………………………………… 
………………………………………………………………………………… 
………………………………………………………………………………… 
24. What are some of the challenges of the interventions for malaria control? 
………………………………………………………………………………… 
………………………………………………………………………………… 
………………………………………………………………………………… 
………………………………………………………………………………… 
………………………………………………………………………………… 
 
 
 
 
 
                                                                      
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APPENDIX II 
 
                                                    In-depth interview 
Interviewer:………………………………………………………………….………. 
Date:………………………………………………………………………………….. 
District:…………………………………………………………..…………………... 
Interviewee:………………………………………………………………………….. 
Position:……………………………………………………………………………….. 
 1. What is malaria and what causes it in your area? 
2. What is the age group that is more infected by malaria? 
3. What is the gender that is more infected by malaria? 
4. What are control measures that are used in your areas? 
5. What are most used control measures? 
6. What are some of the challenges of the interventions for malaria control? 
7. What coping strategies have you adopted on your own to manage malaria? 
8. Do you think the various ways of malaria control have brought about reduction of 
the disease? 
9. How effective is the treatment you give? 
 
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