A spatial decision support system for malaria elimination
Gerard Charles Kelly
A thesis submitted for the degree of Doctor of Philosophy at
The University of Queensland in 2013
School of Population Health Abstract
Background
Following renewed international attention and political commitment, malaria elimination is back on the world health agenda. Whilst there is currently a global focus and dedication of resources towards elimination, malaria programs pursuing this goal face significant challenges in meeting increased operational requirements, particularly in resource-poor and remote settings. Key priorities of elimination include the need to ensure the effective delivery of scaled-up services and interventions at optimal levels of coverage in target areas; the ability to rapidly identify transmission foci and target appropriate responses; and the capacity to readily provide detailed and accurate data to generate useful information, knowledge and evidence throughout all phases of program implementation. A need for further research into new tools and approaches to support intensified malaria control and elimination is identified within the Roll Back Malaria (RBM) Global
Malaria Action Plan (GMAP) as a core global strategy.
Aims
Aims of the thesis were to develop and implement a spatial decision support system (SDSS) for malaria elimination to guide program priorities in Solomon Islands and Vanuatu including: modern geographical reconnaissance (GR) mapping and data collection; frontline vector-control and malaria prevention intervention management; and high resolution geospatial surveillance-response.
Methods
Customized geographic information system (GIS) based SDSS were developed at a provincial level to support progressive malaria elimination campaigns in Solomon Islands and Vanuatu.
Geographical Reconnaissance (GR) surveys were conducted in the elimination provinces of
Temotu, Solomon Islands and Tafea, Vanuatu in 2008 and 2009 to rapidly map and enumerate households and collect associated population and household structure data using integrated handheld computers and global positioning systems (GPS). A SDSS approach was adopted to guide
ii the planning, implementation and assessment of frontline focal indoor residual spraying (IRS) interventions on Tanna Island, Vanuatu in 2009. High-resolution surveillance-response systems were also developed in the elimination provinces of Temotu and Isabel, Solomon Islands and Tafea,
Vanuatu in 2011 to support rapid reporting and mapping of confirmed cases by household, automatic classification and mapping of active transmission foci, and the generation of areas of interest (AOI) regions to conduct targeted response. Quantitative and qualitative analysis were conducted throughout the course of the thesis to assess the performance and acceptability of the
SDSS-framework. A retrospective overall examination of the customised SDSS applications developed to support elimination in Solomon Islands and Vanuatu was also conducted in 2013 to review the role of SDSS for malaria elimination.
Results
A total of 10,459 households were mapped and enumerated, with a population of 43,497 and 30,663 household structures recorded and uploaded into the SDSS framework during three GR surveys.
Household maps, as well as detailed summaries were extracted from the SDSS and used to describe the spatial distribution of the target population in the elimination provinces. A map-based SDSS application was used identify the focal IRS boundary on Tanna Island and delineate 21 individual operation areas comprising 187 settlements and 3,422 households. Household distribution maps, data summaries and checklists were generated to support IRS implementation. Spray coverages of
94.4% of households and 95.7% of the population were achieved. Spray status maps were also produced at a sub-village level to visualise the delivery and coverage of IRS by household. A total of 183 confirmed cases were reported and mapped in the SDSS and used to classify active transmission foci within a target population of 90,354. Automated AOI regions were also generated to identify response areas. Of the reported confirmed cases, 82.5% were successfully mapped at the household level, with 100% of remaining cases geo-referenced at a village level. By 2013, a total of
20,733 households, 55,711 structures and a population of 91,319 were recorded and mapped in the
SDSS in four elimination provinces in Solomon Islands and Vanuatu. The framework has been used
iii to guide both IRS and long-lasting insecticidal net (LLIN) distribution achieving an overall household coverage of 97.5% and 91.7% respectively. High-resolution surveillance-response applications are also ongoing. A high acceptability of the SDSS was recorded from stakeholder surveys and group discussions.
Conclusions
This thesis presents an SDSS-based approach to addressing scaled-up demands of elimination utilising modern geospatial tools and technology in remote and challenging settings. Geospatial systems developed to support Pacific Island progressive malaria elimination campaigns demonstrate the suitability of a SDSS-framework as a platform to rapidly collect, store and extract essential data throughout key phases of program implementation; effectively manage and ensure essential services are delivered at optimal levels of coverage in target areas; and actively locate and classify transmission to guide swift and appropriate responses. Findings presented in the thesis also highlight the importance of the integral role of malaria program personnel, and the need to transition from traditional styles of monitoring and evaluation to active surveillance-response using minimal essential data integrated using modern SDSS technology.
iv Declaration by author
This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis.
I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my research higher degree candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.
I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the General Award Rules of The University of Queensland, immediately made available for research and study in accordance with the Copyright Act 1968.
I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis.
v Publications during Candidature
Peer-reviewed publications:
Clements AC, Reid H, Kelly GC, Hay SI: Further shrinking the malaria map: how can geospatial science help achieve malaria elimination? The Lancet Infectious Diseases 2013, 13:709-718.
Kelly GC, Hale E, Donald W, Batarii W, Bugoro H, Nausien J, Smale J, Palmer K, Bobogare A,
Taleo G, Vallely A, Tanner M, Vestergaard L, Clements AC: A high-resolution geospatial surveillance-response system for malaria elimination in Solomon Islands and Vanuatu. Malar J
2013, 12:108.
Kelly GC, Tanner M, Vallely A, Clements AC: Malaria elimination: moving forward with spatial decision support systems. Trends in Parasitology 2012, 28:297-304.
Kelly GC, Moh Seng C, Donald W, Taleo G, Nausien J, Batarii W, Iata H, Tanner M, Vestergaard
LS, Clements AC: A spatial decision support system for guiding focal indoor residual spraying interventions in a malaria elimination zone. Geospat Health 2011, 6:21-31.
Kelly GC, Hii J, Batarii W, Donald W, Hale E, Nausien J, Pontifex S, Vallely A, Tanner M,
Clements AC: Modern geographical reconnaissance of target populations in malaria elimination zones. Malar J 2010, 9:289.
Reid H, Vallely A, Taleo G, Tatem A, Kelly G, Riley I, Harris I, Henri I, Iamaher S, Clements A:
Baseline spatial distribution of malaria prior to an elimination programme in Vanuatu. Malaria
Journal 2010, 9:150.
vi
Henri I, Iamaher S, Iavro J, Kalomuana MJ, Mera K, Taleo G, Alungo B, Bobogare A, Boaz
L, Dagi E, Hou B, Leo B, Memua N, Moses S, Newa A, Osiwane L, Raoga R, Rose N,
Salopuka L, Tanen A, Young J, Atkins C, Auliff A, Bain LM, Cheng Q, Cooper R, Harris IE,
Ebringer A, Edstein MD, Elmes N, Gray KA, Humphries J, Johnson ML, Krause D, Lilley K,
MacKenzie D, McPherson B, Perrin R, Shanks GD, Sharrock WW, Staley J, Waters NC,
Atkinson JA, Clements A, Dove G, Forsyth S, Kelly G, Reid LM, Riley I, Vallely A,
Whittaker M. Malaria on isolated Melanesian islands prior to the initiation of malaria elimination activities. Malar J 2010, 9:218.
Conference Abstracts and Technical Meetings:
Kelly GC: Locating and targeting hotspots and hotpops with spatial decision support systems. Asia
Pacific Malaria Elimination Network (APMEN) Annual Business and Technical Meeting, March 4
– 7, 2013, Bali, Republic of Indonesia.
Kelly GC, Hale E, Donald W, Clements AC: Facing the operational challenges of Pacific Island malaria elimination with spatial decision support systems. Australian Agency for International
Development (AusAID) Malaria 2012: Saving Lives in the Asia-Pacific, October 31 – November 2,
2012, Sydney, Australia (Poster Presentation).
Kelly GC, Hale E, Donald W, Taleo G, Bobogare A, Baquilod M, Vestergaard LS: Modern geographical reconnaissance for malaria control and elimination in the Asia-Pacific: strengthening operational capacity through practical inter-country collaboration. Australian Agency for
vii International Development (AusAID) Malaria 2012: Saving Lives in the Asia-Pacific, October 31 –
November 2, 2012, Sydney, Australia (Poster Presentation).
Kelly GC: Innovative approaches to surveillance, monitoring and evaluation: GIS for malaria elimination in Solomon Islands and Vanuatu. World Health Organization (WHO) Western Pacific
Region Malaria Programme Manager’s Meeting, August 10 – 12, Manila, Philippines.
Kelly GC: Spatial Decision Support Systems: Research and applications in Solomon Islands and
Vanuatu: 5th Malaria Reference Group Meeting, May 23 – 27, 2010, Brisbane, Australia.
viii Publications Included in this Thesis
Kelly GC, Tanner M, Vallely A, Clements AC: Malaria elimination: moving forward with spatial decision support systems. Trends in Parasitology 2012, 28:297-304. Incorporated as Chapter 2.
GK was responsible for 90% of the concept design, 100% of the research and review of the
literature, and 90% of the manuscript drafting. ACAC, MT & AV were responsible for 10%
of the concept design and 10% of the manuscript drafting through the provision of scientific
guidance and commentary on manuscript drafts.
Kelly GC, Hii J, Batarii W, Donald W, Hale E, Nausien J, Pontifex S, Vallely A, Tanner M,
Clements AC: Modern geographical reconnaissance of target populations in malaria elimination zones. Malar J 2010, 9:289. Incorporated as Chapter 3.
GK was responsible for 85% of the study concept design, 85% of field research operations
and 85% of manuscript drafting. ACAC, JH, AV and MT were responsible for 15% of the
study concept design through detailed scientific guidance. EH, JN, WB, & WD were
responsible for 15% of field research operations. ACAC, JH, MT, AV were responsible for
10% of manuscript drafting through the provision of detailed commentary. SP, EH, JN, WB,
& WD were responsible for 5% of manuscript drafting through the provision of additional
commentary.
Kelly GC, Moh Seng C, Donald W, Taleo G, Nausien J, Batarii W, Iata H, Tanner M, Vestergaard
LS, Clements AC: A spatial decision support system for guiding focal indoor residual spraying interventions in a malaria elimination zone. Geospat Health 2011, 6:21-31. Incorporated as Chapter
4.
GK was responsible for 90% of the study concept design, 80% of field research operations
and 85% of manuscript drafting. ACAC, MSC, LV, AV, MT and GT were responsible for
ix 10% of the study concept design through detailed scientific guidance. HI, WD, JN and WB
were responsible for 20% of field research operations. ACAC, MSC, LV, AV and MT were
responsible for 10% of manuscript drafting through the provision of detailed commentary.
WD, GT, JN, WB and HI were responsible for 5% of manuscript drafting through the
provision of additional commentary.
Kelly GC, Hale E, Donald W, Batarii W, Bugoro H, Nausien J, Smale J, Palmer K, Bobogare A,
Taleo G, Vallely A, Tanner M, Vestergaard L, Clements AC: A high-resolution geospatial surveillance-response system for malaria elimination in Solomon Islands and Vanuatu. Malar J
2013, 12:108. Incorporated as Chapter 5.
GK was responsible for 90% of the study concept design, 80% of field research operations
and 85% of manuscript drafting. ACAC, MT, AV, LV, KP, AB and GT were responsible for
10% of the study concept design through detailed scientific guidance. EH, WD, WB, JN,
HB and JS were responsible for 20% of field research operations. ACAC, LV, AV and MT
were responsible for 10% of manuscript drafting through the provision of detailed
commentary. KP, JS, AB, GT, EH, HB, WD, WB, and JN were responsible for 5% of
manuscript drafting through the provision of additional commentary.
x Contributions by Others to the Thesis
No contributions by others.
Statement of Parts of the Thesis Submitted to Qualify for the Award of Another Degree
None.
xi Acknowledgements
There are many to thank for their significant role in this PhD journey. Firstly, I would like to thank my principal advisor Professor Archie Clements, whose wisdom and direction throughout this entire process has been invaluable. I also extend my deepest gratitude to my associate supervisors
Professor Marcel Tanner, Associate Professor Andrew Vallely, and Dr Lasse Vestergaard for their continuous mentorship and guidance. I would also like to thank (the now retired ) World Health
Organisation (WHO) Malaria Scientists Dr Jeffrey Hii and Dr Moh Seng Chang, for their encouragement, belief and support, right from the very start.
Importantly, I sincerely thank the communities of Isabel and Temotu Provinces, Solomon Islands, and Tafea Province, Vanuatu. The degree of warmth, enthusiasm and positivity received whilst working within these communities has been unparalleled in my lifetime. The unforgettable experiences, values and lessons I have learnt during these times extend far beyond the contents of this thesis. I would also like to thank my colleagues from the Solomon Islands and Vanuatu Vector
Borne Diseases Control Programmes, without whom, this research would not have been possible. In particular I thank my counterparts Mr Erick Hale, Mr Wesley Donald, Mr Willie Batarii, Mr Johnny
Nausien, Mr Watson Hevalao and Mr Andrew Newa. Taggio tumas olketa frens. I remain committed and dedicated to the pursuit of elimination in your countries and look forward to continue to work with you all to achieve this goal. I would also like thank my colleagues whom I have worked (both former and current) from the Pacific Malaria Initiative Support Centre, especially Professor Maxine Whittaker, Dr Tanya Russell, Dr Rushika Wijesinghe, Mr Luke
Marston, and Mr John Smale.
Finally, I would like to thank my family for their unconditional support, understanding and patience throughout the entirety of this endeavour. In particular, Mum, Dad, and Lou.
xii Keywords
malaria elimination, spatial decision support systems, geographic information systems, epidemiology, surveillance, malaria, disease control
Australian and New Zealand Standard Research Classifications (ANZSRC)
ANZSRC code: 111711 Health Information Systems (incl. Surveillance) - 35%
ANZSRC code: 111706 Epidemiology - 35%
ANZSRC code: 090903 Geospatial Information Systems - 30%
Fields of Research Classification
FoR code: 1117 Public Health and Health Services – 100%
xiii Table of Contents
Abstract ii
Declaration by Author v
Publications During Candidature vi
Publications Included in this Thesis ix
Contributions by Others to the Thesis xi
Statement of Parts of the Thesis Submitted to Qualify for the Award of Another Degree xi
Acknowledgements xii
Keywords xiii
Australian and New Zealand Standard Research Classifications (ANZSRC) xiii
Fields of Research Classification xiii
Table of Contents xiv
List of Figures xvi
List of Tables xviii
List of Boxes xx
List of Abbreviations xxi
Chapter 1 Introduction 1
Chapter 2 Malaria elimination: moving forward with spatial decision support
systems 25
Chapter 3 Modern geographical reconnaissance of target populations in malaria
elimination zones 36
Chapter 4 A spatial decision support system for guiding focal indoor residual
spraying interventions in a malaria elimination zone 52
Chapter 5 A high-resolution geospatial surveillance-response system for malaria
elimination in Solomon Islands and Vanuatu 67
Chapter 6 Spatial decision support systems: a way forward to tackle operational
xiv
challenges of malaria elimination – a Pacific experience 86
Discussion 106
Complete Thesis Reference List 129
Appendix A An overview of WHO recommended interventions by programme type to
support the transition from malaria control to elimination 147
Appendix B Additional File Material published as part of the peer-reviewed article
forming Chapter 3: Demographic description of the population of
Solomon Islands and Vanuatu Geographical Reconnaissance operation
areas, 2009 149
Appendix C Additional File Material published as part of the peer-reviewed article
forming Chapter 3: Household structures summary table of Solomon
Islands and Vanuatu Geographical Reconnaissance operation areas, 2008-
2009 150
Appendix D Long lasting insecticidal net (LLIN) distribution SDSS household
coverage data generated in the Temotu Province SDSS, 2000 - 2010 151
Appendix E Long lasting insecticidal net (LLIN) distribution SDSS household
coverage maps generated using the Temotu Province SDSS, 2009 – 2010 152
Appendix F Additional File Material published as part of the peer-reviewed article
forming Chapter 5: Screenshot of the Isabel Province surveillance- 157
response spatial decision support system user interface
Appendix G Additional File Material published as part of the peer-reviewed article
forming Chapter 5: Screenshot of the Temotu Province rapid case 158
surveillance reporting digital data entry form template
Appendix H Screenshot of the Isabel Province surveillance-response spatial decision
support system user interface illustrating the map-based and query-
dialogue tools to support epidemiological investigation 159
xv
List of Figures
CHAPTER 1
Figure 1 Malaria programme phases and milestones on the path the malaria
elimination 4
Figure 2 Basic schematic of a Spatial Decision Support System Framework 8
Figure 3 Spatial Decision Support System for Malaria Elimination OR/IR
Research Structure 13
CHAPTER 2
Figure 1 Geographical reconnaissance (GR) mapping: traditional processes versus
new technology 30
Figure 2 A conceptual spatial decision support system (SDSS) framework for
malaria elimination 31
Figure 3 Screenshot of Isabel Province, Solomon Islands SDSS 33
CHAPTER 3
Figure 1 General location map of Geographical Reconnaissance Operation Areas 42
Figure 2 ArcPad 7.0 Digital Data Collection Form developed for Geographical
Reconnaissance Operations 43
Figure 3 Snapshot of automated GIS mapping application for Geographical
Reconnaissance Operations 44
Figure 4 Santa Cruz, Temotu Province, Solomon Islands Population Distribution
Map 46
Figure 5 Tanna GR Operation Zone, Tafea Province, Vanuatu Population
Distribution Map 47
Figure 6 Example Village Household Distribution Map 48
xvi
CHAPTER 4
Figure 1 Location of Tanna Island, Vanuatu 58
Figure 2 Tanna Island IRS operation areas map 60
Figure 3 Screenshot of the IRS planning SDSS application 61
Figure 4 Spatial distribution of IRS coverage prior to and following the first-round
follow-up operations 61
Figure 5 Screenshot of the IRS household follow-up SDSS application 63
Figure 6 Example Tanna Island IRS household status map at a sub-village scale 63
CHAPTER 5
Figure 1 General location map of Solomon Islands and Vanuatu elimination
provinces 74
Figure 2 Spatial decision support system based malaria elimination surveillance-
response framework 76
Figure 3 Malaria case distribution map, Isabel Province, Solomon Islands, 2011 78
Figure 4 Malaria case distribution map, Temotu Province, Solomon Islands, 2011 79
Figure 5 Malaria case distribution map, Tafea Province, Vanuatu, 2011 80
Figure 6 Current Area of Interest (AOI) map, Isabel Province, Solomon Islands, 81
31/12/2012
Figure 7 Proportion of positive cases per month against detected AOI regions of 83
varying geographic radius
CHAPTER 6
Figure 1 Example screenshots of the map-based SDSS user interface used to
support priority frontline intervention management (1A); and high-
resolution surveillance and response (1B) 98
xvii
List of Tables
CHAPTER 3
Table 1 Summary of data collected during Solomon Islands and Vanuatu
Geographical Reconnaissance operations, 2008-2009 46
Table 2 Solomon Islands and Vanuatu Geographical Reconnaissance field
procedures summary, 2008-2009 48
CHAPTER 4
Table 1 Technical components of the indoor residual spraying (IRS) customised
spatial decision support system (SDSS) 58
Table 2 Operation area summary data generated by the customised SDSS 60
Table 3 Tanna Island first-round IRS coverage, stratified by health zone 62
Table 4 IRS coverage breakdown by household structure 62
Table 5 Reasons households were not sprayed at the completion of IRS 63
Table 6 SDSS user acceptability survey response summary data 63
CHAPTER 5
Table 1 Summary of the technical components of the malaria elimination
geospatial surveillance-response SDSS 75
Table 2 Active and residual non-active transmission foci classification parameters
and nominated response interventions adopted in Isabel province
surveillance-response SDSS 77
Table 3 Breakdown of 2011 case species type and suspected mode of transmission 78
Table 4 Proportion of cases detected and associated population and households
located within SDSS generated areas of interest (AOI) of varying radius 82
Table 5 Time taken to report cases from health facility to Isabel provincial office
during 2011 83
xviii
Table 6 Proportion of 2011 reported positive malaria cases successfully geo-
located at the household level 83
CHAPTER 6
Table 1A Summary of spatial decision support system technical functions and
applications developed to support specific operational priorities of
malaria elimination in Solomon Islands and Vanuatu 95
Table 1B Summary of spatial decision support system user acceptability surveys
and stakeholder focus group discussions and informal interviews
conducted in Solomon Islands and Vanuatu 99
xix
List of Boxes
CHAPTER 2
Box 1 Example of spatial decision support system (SDSS) applications in
malaria and vector-borne disease management 31
Box 2 Supporting Pacific Island malaria elimination with spatial decision
support systems (SDSS) 31
CHAPTER 6
Box 1 Summary of main lessons learned 91
xx
List of Abbreviations
ACD Active Case Detection AOI Area of Interest AusAID Australian Agency for International Development BCC Behaviour Change Communication DDT Dicholoro-dephenyl-trichloroethane DSS Decision Support System FSAT Focal Screening and Treatment GFTAM The Global Fund to fight AIDS, Tuberculosis and Malaria GIS Geographic Information System GMAP Global Malaria Action Plan GMEP Global Malaria Eradication Programme GPS Global Positioning System GR Geographical Reconnaissance HIS Health Information System IR Implementation Research IRS Indoor Residual Spraying LLIN Long Lasting Insecticidal Net M&E Monitoring and Evaluation MHMS Ministry of Health and Medical Services MoH Ministry of Health NMP National Malaria Program NVBDCP National Vector Borne Diseases Control Programme OR Operations Research PacMI Pacific Malaria Initiative PacMISC Pacific Malaria Initiative Support Centre PCD Passive Case Detection PDA Personal Digital Assistant RBM Roll Back Malaria RDT Rapid Diagnostic Test RS Remote Sensing SDSS Spatial Decision Support System SI Solomon Islands SM&E Surveillance Monitoring and Evaluation SMS Short Message Service SOP Standard Operating Procedure UCSF University of California, San Francisco VBDCP Vector Borne Diseases Control Programme WHO World Health Organization WHOPES World Health Organization Pesticide Evaluation Scheme
xxi
Chapter 1
Introduction
1.1 Background
The burden of malaria continues to be a leading contributor of global morbidity and mortality
today, remaining inextricably linked to poverty, and social and economic development [1-4].
Malaria is one of the oldest and most significant infectious diseases to affect humanity, having accompanied the human species throughout our evolutionary history [5, 6]. Despite this, malaria is an entirely preventable and treatable disease [1].
1.1.1 The Global Malaria Eradication Period
Since the discoveries in the late 19th century of the cause of malaria, parasites from the genus
Plasmodium, and the role of mosquitoes as the mode of transmission, there have been
significant advancements in the understanding, diagnosis, treatment and control of malaria [6,
7]. Following the discovery of the insecticidal properties of dicholoro-dephenyl-
trichloroethane (DDT) in 1939 and the subsequent widespread early success of indoor
residual spraying (IRS), the World Health Organisation (WHO) launched the Global Malaria
Eradication Programme (GMEP) in 1955 [6-8]. The GMEP was rolled out largely as a
vertical campaign that focused predominantly on the implementation of IRS using DDT and
followed a fundamental doctrine of complete intervention coverage and perfect execution in
eradication areas [7, 9]. Many countries, outside of mainland Africa and New Guinea, joined
the GMEP, and the programme led to widespread reductions in global mortality and
1 morbidity, and contributed to the elimination of malaria from Europe, North America, the
Caribbean and parts of Asia and South-Central America [5, 6, 10].
Despite these successes, the GMEP campaign began to falter in many areas due to factors such as the emergence of insecticide-resistant mosquitoes, drug resistant parasites, technical problems associated with complex vector behaviour (e.g. outdoor biting and resting) and operational deficiencies, particularly in remote and resource-poor countries [2, 6-11]. As a result, the WHO reverted the GMEP strategy in 1969 to focus on malaria control in the countries where eradication was no longer seen as feasible within the foreseeable future [6, 7,
12]. This re-focusing, coupled with waning political and donor commitment and global economic downturns, saw a significant resurgence of malaria following the eradication period, including in some countries, such as Sri Lanka, where transmission had been successfully interrupted for a period of time [6, 7, 11].
Experiences from the GMEP period have provided valuable insight and lessons for malaria programmes today. Common traits of successful programs during the GMEP period included dedicated political commitment and public support, functioning and robust health systems, adequate resources and personnel, good organisational structures and somewhat favourable existing epidemiological conditions [6]. Conversely, countries whose efforts faltered were often less stable politically and were characterised by inadequate human resources, constrained health systems, and often a failure to effectively monitor operational activities and the respective epidemiological situations [6, 13]. Of particular importance, this period highlighted that no single approach to eradication was applicable across all regions [7].
Lessons from the GMEP have also shown that effective strategies require flexibility, ownership by local programmes and communities, a detailed understanding of local
2 epidemiological settings, adequate integration with existing health systems and infrastructure,
the need for ongoing surveillance, and long-term political and financial commitment [7].
1.1.2 Malaria elimination today
In the decades following the GMEP, the worsening malaria situation, coupled with
advancements in drug and vaccine development, vector control and insecticide-treated bed
nets during this time, saw a renewed global focus on malaria control throughout the 1990s
[10]. The launching of the Roll Back Malaria (RBM) initiative in 1998, and the increased
dedication of resources and funding over the past 15 years through institutions such as the
Global Fund to fight Aids Tuberculosis and Malaria (GFTAM), the Bill and Melinda Gates
foundation, the Presidents Malaria Initiative and others, has once again reinvigorated calls for
the global elimination of malaria [10, 14-17].
This renewed international commitment, coupled with the lessons learnt from the GMEP period, has helped develop contemporary strategies and priorities to support intensified malaria control and elimination initiatives at a global scale [6, 14-16, 18, 19]. These current strategies are outlined in the RBM Global Malaria Action Plan (GMAP) and include: the scaling up and sustainment of intensive malaria control operations; progressively eliminating malaria from the endemic margins inward (i.e. shrinking the malaria map); and the continuation of research into new tools and approaches to malaria control and elimination
[15, 20]. The past decade has seen significant gains in the overall reduction of malaria [13,
17, 21]. Currently, 34 countries are now pursuing malaria elimination at either a national or sub-national scale. [17, 21, 22].
3 Malaria elimination is defined by the WHO as the interruption of local mosquito-borne transmission within a designated geographical area as a result of “deliberate efforts” [19, 23].
For WHO certification of malaria elimination, a country must demonstrate that there has been no evidence of local transmission for a period of three years [23]. WHO recommendations provide guidelines for low to moderate endemic countries on how to manage the transition from malaria control to elimination, based on indicative milestones by programme type
(Figure 1, [23]). As countries transition from control to the pre-elimination, elimination and eventual prevention of reintroduction phases, programs are required to refocus priorities and strategies accordingly; with the stage of elimination dictating specific interventions for case management, vector control and prevention, monitoring and evaluation, and health system requirements [23]. Appendix A provides an overview of WHO recommended interventions by programme type.
Figure 1: Malaria programme phases and milestones on the path the malaria elimination [23]
4 1.1.3 Operational priorities of malaria elimination
Whilst the decision of a country to move from malaria control to elimination is not an obligation and in many cases not yet feasible [23], those countries committing to elimination require significant modification and intensification of key strategies and interventions to ensure success [15]. Operational priorities of malaria elimination include the need to ensure the effective delivery of scaled-up services and interventions at optimal levels of coverage in target areas; the ability to actively identify transmission foci and target appropriate responses swiftly; and the capacity to readily provide detailed and accurate data to facilitate decision- making and generate useful information, knowledge and evidence throughout all facets of program implementation [2, 9, 20, 23-26].
Besides the slide positivity rate within a defined geographical area falling below 5%, an indication of a country’s readiness to enter the pre-elimination phase is its capacity to measure and know definitively its malaria incidence rate [20]. During the pre-elimination stage, malaria programmes must also have the ability to reorient themselves to further emphasise surveillance, reporting and information systems [20]. An essential component of contemporary elimination strategies is the need for malaria programmes to move from a traditional style of monitoring and evaluation to a more active mode of surveillance with the capacity to rapidly detect infections, assess trends in transmission and respond accordingly
[16, 25-27]. In the context of elimination, where the goal is to halt localized transmission, it is essential that surveillance systems are able to swiftly detect all infections (both due to local transmission and those that are imported), classify and locate transmission foci, and ensure all cases (symptomatic and asymptomatic) are effectively treated before the occurrence of secondary cases and perpetuation or reintroduction of local transmission [24-27].
5 The increased operational demands and complexities of malaria elimination present significant challenges for programmes pursuing these goals, particularly in the context of the resource-poor and remote settings that characterise many malaria-endemic countries. Robust, dynamic and responsive health systems are required throughout all phases of elimination [9,
23]. As health systems in many endemic countries are often already heavily constrained, additional technical expertise, resources and operational tools become essential to support elimination programmes [9, 18, 28]. It has been acknowledged that weak national health information systems (HIS) limit the availability of reliable data and indicators useful to assessing disease control impact and for monitoring the progress of management initiatives
[29], representing a major obstacle to the scale-up of services in malaria-endemic countries
[30]. To support the successful and progressive transition of countries from control to elimination, a critical need exists to both implement and strengthen integrated systems, and explore additional operational tools and approaches to support key intervention priorities throughout all phases of pre-elimination, elimination and prevention of reintroduction.
1.1.4 Geospatial information management and decision support systems
The spatial heterogeneity and micro-epidemiological variability of malaria transmission have explicit geographic implications for managing intensified control and elimination in regards to efficacy, efficiency and cost [31-39]. Malaria elimination programmes require vigorous surveillance, monitoring and evaluation (SM&E) mechanisms with geographical coverage across entire target areas [23] to successfully meet the operational priorities of elimination.
As such, a spatial component to information systems is desirable essential.
Research on the effects of information presentation mechanisms have demonstrated that map- based formats improve the accuracy and efficiency of complex decision making for tasks
6 where there is a geographical dimension [40-48]. Mapping and geographical reconnaissance operations have been adopted by malaria programmes since the WHO GMEP era to monitor transmission and to support the implementation of malaria prevention and vector control interventions, such as IRS [49, 50]. Geographic information systems (GIS) are integrated collections of hardware, software and data systems designed to capture, analyse, manage and disseminate geographical data [51]. Whilst the relevance of spatial analysis and GIS
technology in vector-borne disease management today is now well recognised [52-64], little research or progress has been made into the feasibility of integrating GIS into decision support systems to guide malaria elimination interventions at an operational level.
Spatial decision support systems (SDSS) provide computerised support for decision making where there is a geographic or spatial component to a decision [65-67]. SDSS are generally based around a GIS that integrates database management systems with analytical models, graphical map display and tabular reporting capabilities, and the expert knowledge of decision makers [65-67] (Figure 2). These systems, when used effectively have the potential to provide health programmes with a powerful operational tool to support the decision making process.
7
Figure 2: Basic schematic of a Spatial Decision Support System Framework
1.1.5 Progressive malaria elimination in the Pacific
To support the global elimination strategy of shrinking the malaria map from the endemic margins inward [15], progressive elimination programmes are currently underway in the
Pacific Island nations of Solomon Islands and Vanuatu, with support from the Australian
Government funded Pacific Malaria Initiative (PacMI). Malariometric surveys carried out in
Temotu Province, Solomon Islands and Tafea, Province, Vanuatu in 2008 revealed low prevalence of malaria in these regions and a favourable situation for local malaria elimination
[68, 69]. Subsequent elimination campaigns commenced in Isabel Province, Solomon Islands in 2009 and Torba Province, Vanuatu in 2012. Key activities selected for the elimination programmes include: the scaling-up of mass distribution of long lasting insecticidal nets
(LLIN) and focal IRS interventions; improved quality and access to malaria diagnostics and
8 effective treatments; increased community engagement and behaviour change communication
(BCC); and the implementation of high-resolution surveillance and response frameworks to strengthen passive and active case detection and reporting, and targeted rapid vector-control and focal screening and treatment (FSAT) response measures [70-72].
Following the decision to pursue progressive elimination, the National Malaria Programs
(NMP) of each country recognised the limitations associated with established malaria control systems and identified the need for additional operational tools and approaches to support the scaled-up objectives. With the support of the Pacific Malaria Initiative Support Centre
(PacMISC) and the WHO, both the Solomon Islands and Vanuatu National Vector Borne
Diseases Control Programmes (NVBDCP) have demonstrated significant interest and commitment to piloting a SDSS-based approach to support the operational priorities of malaria elimination.
1.2 Significance of the research
Knowing where infections are occurring and the need to develop effective operational tools to support malaria programmes are key challenges of contemporary malaria elimination [73].
In the recent scientific literature, authors have called for exploration of novel technologies, approaches and decision-making tools to support intensified malaria control and elimination campaigns [2, 9, 20, 24, 25, 73-75]. Whilst the advantages of mapping and GIS in health care analysis, surveillance and planning are now acknowledged, the potential applications in spatial epidemiology go beyond current applications and require further exploration and research [64]. Despite a recognition of the value of mapping, improved surveillance and the importance of maintaining a geographical focus for key interventions [9, 23-27], little
9 research or knowledge currently exists on how to utilise and operationalize GIS-based
technology for malaria elimination at a program implementation level, particularly in the
context of remote and resource-poor settings.
All elements of malaria elimination management have a spatial component. Examples include: the allocation of resources to support the effective implementation of priority
interventions such as LLIN distribution, IRS operations and focal screening and treatment
(FSAT); the monitoring and evaluation of intervention and service delivery coverage;
entomological surveillance and targeted vector control; malaria case surveillance and
assessing changes in transmission; the strategic targeting of appropriate rapid response
measures based on spatiotemporal patterns of malaria distribution; and spatial monitoring of
antimalarial drug efficacy and insecticide resistance.
A key strategy of the Global Malaria Action Plan (GMAP) is for the continuation of research
into new tools and approaches to malaria control and elimination [15, 20]. Therefore, the
opportunity to develop, implement and evaluate the feasibility of SDSS for guiding malaria
elimination operations is in line with global malaria elimination strategies. With the support
of both the Solomon Islands and Vanuatu NVBDCP, an ideal opportunity exists to actively
research and pilot new SDSS-based operational tools and approaches for malaria elimination.
It is anticipated that the findings from this research will have practical relevance for national
health programmes pursuing the global agenda of intensified malaria control and elimination,
as well as expanded applications for vector-borne and infectious disease management in
general.
10 1.3 Hypothesis and aim of the research
A major challenge in health service delivery in resource-poor environments is the practical implementation of existing tools and knowledge about disease into real-world settings where the end user (i.e. a target population) is able to benefit [76]. Whilst it is acknowledged that a scaling up of interventions and surveillance is necessary for malaria elimination, the practical implications on how to successfully implement, and effectively monitor these activities is still unclear. It is envisaged that the implementation of an integrated SDSS for malaria elimination at a programme management level will provide a new, practical and powerful decision making tool to guide key interventions throughout all phases of elimination. This thesis proposes that a SDSS will provide an operational tool to support the effective implementation and monitoring of priority interventions at the level of detail required to satisfy malaria elimination outcomes.
1.4 Specific research objectives
This thesis examines the role of a SDSS framework in meeting the operational priorities of malaria elimination, using examples from the Solomon Islands and Vanuatu progressive malaria elimination campaigns. Specific objectives of the study will be to:
1. Conduct Geographical Reconnaissance (GR) using modern mapping technology to
map, define and quantify target populations and household structures in the
elimination provinces of Solomon Islands and Vanuatu;
11 2. Develop an integrated GIS-based SDSS to a provide a framework for the coordination
and implementation of scaled-up elimination interventions in Solomon Islands and
Vanuatu;
3. Adopt a SDSS approach for the monitoring and evaluation of priority vector-control
and malaria prevention interventions including focal Indoor Residual Spraying (IRS)
and Long Lasting Insecticidal Net (LLIN) distribution;
4. Support real-time passive and active malaria case detection, high-resolution
surveillance-response and investigation through an interactive SDSS; and
5. Evaluate the application and effectiveness of an SDSS framework approach for
monitoring priority malaria elimination indicators and guiding key interventions.
1.5 Structure of the thesis
Research into the application of SDSS for malaria elimination was structured around an action-based operations and implementation research (OR/IR) framework [77] to support the current elimination programmes in Solomon Islands and Vanuatu. This framework consists of three major phases: planning, implementation, and follow-through. The thesis is comprised of seven chapters. Chapters 2 to 5 consist of manuscripts that have been published in peer- reviewed scientific journals. Chapter 6 also consists of a manuscript that is currently being prepared for submission to a peer-reviewed journal. Chapter 2 has been written and published as an independent review article. Chapters 3 to 6 all incorporate a combination of an introductory literature review, aims, methods, results and discussion. Whilst all chapters
12 have been written to form a cohesive thesis, each published manuscript is also intended to stand alone as an independent article, which may lead to some descriptive repetition. A general introduction and background to the research, as well as a summary of the overall findings, implications, limitations and future recommendations are also included in the thesis as Chapters 1 and 7 respectively. Figure 3 illustrates the thesis structure in relation to the
OR/IR framework.
Figure 3: Spatial Decision Support System for Malaria Elimination
Proposed OR/IR Research Structure
13 Specifically, Chapter 1 provides a general overview of the global malaria elimination agenda,
briefly describing the operational priorities and challenges faced by health programmes today
from a geospatial perspective. This chapter also highlights the potential roles of mapping and
SDSS to support these operational priorities, introduces the Pacific Island progressive malaria
elimination campaigns in Solomon Islands and Vanuatu, and details the research aims and
objectives of the thesis.
Chapter 2 includes a review article published in Trends in Parasitology. This paper expands
upon the concepts introduced in Chapter 1 to explore geospatial operational challenges faced
by malaria programmes today, contemporary approaches to mapping malaria using GIS technology, the role of GR as an operational tool for malaria elimination, and SDSS
applications for vector-borne disease management. In addition, this chapter presents an
SDSS-based framework for malaria elimination.
Chapters 3, 4 & 5 outline the practical implementation of specific components of an SDSS-
framework developed to support operational priorities of malaria elimination in Solomon
Islands and Vanuatu. These chapters are presented as stand-alone published research articles,
with each manuscript including a brief background in relation to the specific operational
priority of malaria elimination addressed, the research methods, results and discussion.
Chapter 3, published in Malaria Journal, outlines the implementation and role of modernised
GR within a SDSS framework for malaria elimination in Solomon Islands and Vanuatu.
Chapter 4, published in Geospatial Health, examines the application of a customised SDSS
application to support the optimal delivery and assessment of focal IRS interventions on
Tanna Island, Vanuatu as part of their respective elimination campaign. Chapter 5, published
in Malaria Journal, explores the application of a high-resolution geospatial surveillance-
14 response system developed within an SDSS-framework in Isabel Province, Solomon Islands to actively identify and map individual malaria infections and transmission foci, and support targeted appropriate responses.
Chapter 6 provides an overall retrospective examination of the customised SDSS applications that have been developed to support malaria elimination in Solomon Islands and Vanuatu
between 2008 and 2013. This paper outlines progress made by the two countries in using an
integrated SDSS framework for progressive malaria elimination, including GR household mapping and data collection operations, priority intervention management (focal IRS and
LLIN distribution) and high-resolution geospatial surveillance; and explores user- acceptability, operational capacity and technical development issues associated with the implementation of the SDSS at a programme level. This chapter is then followed by an expanded discussion and conclusion on the overall role of SDSS for malaria elimination, limitations of the research, and a discussion of future research needs (Chapter 7).
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24
Chapter 2
Malaria elimination: moving forward with spatial decision support systems
2.1 Context
The thesis literature review is presented in this chapter as a published narrative review that examines the operational challenges facing malaria elimination programmes from a geospatial perspective. Key challenges explored include: the need to ensure priority frontline interventions and services are delivered in elimination areas at optimal levels of coverage; the importance of high-resolution surveillance and response to effectively locate and classify malaria transmission and accurately target appropriate responses accordingly; and the need for improved access to quality data and information to support effective decision making and management at a program implementation level. The significance of mapping as an important epidemiological tool is reviewed with specific references to Dr John Snow’s historical Broad
Street water pump cholera maps, contemporary mapping applications using geographic information systems (GIS) adopted for malaria control and elimination today, and the role of geographical reconnaissance (GR) as an operational tool for malaria elimination. Limitations such as a historical lack of access to high-resolution spatial data and manual data collection methods, as well as the limited role of advanced GIS applications at a programme implementation level are explored in relation to modern advances in geospatial technologies.
The potential role of spatial decision support systems (SDSS) for malaria elimination is then explored in this chapter. Existing SDSS-based applications in malaria and vector-borne disease management and the increasing availability of geospatial tools and technologies are described in the context of the specific operational priorities and challenges facing current
25 malaria programmes. The review concludes with a proposal to develop a SDSS framework for malaria elimination.
2.2 Details of Publication
This chapter was published in the peer-reviewed journal Trends in Parasitology in 2012.
The published manuscript has also been highlighted by the following:
- The June 2012 “Resource of the Month” on the University of California, San
Francisco (UCSF) Global Health Group Malaria Elimination Group website
(http://www.malariaeliminationgroup.org/resources/resource-month); and
- A feature article on the Elsevier Malaria Nexus website in September 2012
(http://www.malarianexus.com/)
Kelly GC, Tanner M, Vallely A, Clements A: Malaria elimination: moving forward
with spatial decision support systems. Trends in Parasitology 2012, 28:297-304
Summary
Operational challenges facing contemporary malaria elimination have distinct
geospatial elements including the need for high-resolution location-based
surveillance, targeted prevention and response interventions, and effective delivery of
essential services at optimum levels of coverage. Although mapping and geographical
reconnaissance (GR) has traditionally played an important role in supporting malaria
control and eradication, its full potential as an applied health systems tool has not yet
been fully realised. As accessibility to global positioning system (GPS), geographic
information system (GIS) and mobile computing technology increases, the role of an
26 integrated spatial decision support system (SDSS) framework for supporting the increased operational demands of malaria elimination requires further exploration, validation and application; particularly in the context of resource-poor settings.
Keywords
Malaria elimination; spatial decision support system; geographic information systems; surveillance; monitoring and evaluation
URL: http://www.cell.com/trends/parasitology//retrieve/pii/S1471492212000669?cc=y
27
Review
Malaria elimination: moving forward
with spatial decision support systems
1 1,2,3 4 1
Gerard C. Kelly , Marcel Tanner , Andrew Vallely and Archie Clements
1
University of Queensland, School of Population Health, Brisbane, Australia
2
Swiss Tropical and Public Health Institute, Basel, Switzerland
3
University of Basel, Basel, Switzerland
4
University of New South Wales, The Kirby Institute for Infection and Immunity in Society, Sydney, Australia
Operational challenges facing contemporary malaria to existing national health information systems (HISs) in
elimination have distinct geospatial elements including malaria endemic countries [10,11].
the need for high-resolution location-based surveillance, The heterogeneous nature of malaria transmission and
targeted prevention and response interventions, and microepidemiological variability between target areas
effective delivery of essential services at optimum levels have long been acknowledged as being important problems
of coverage. Although mapping and geographical recon- for planning appropriate interventions [12,13]. For elimi-
naissance (GR) has traditionally played an important role nation, programmes must have an ability to effectively and
in supporting malaria control and eradication, its full swiftly identify, locate and eliminate transmission, mostly
potential as an applied health systems tool has not yet through a monitoring and surveillance strategy of routine-
been fully realised. As accessibility to global positioning ly reported passive case data and targeted active case
system (GPS), geographic information system (GIS) and detection [14]. Even more importantly, contemporary elim-
mobile computing technology increases, the role of an ination programmes also require robust surveillance–
integrated spatial decision support system (SDSS) response mechanisms to effectively locate and eliminate
framework for supporting the increased operational all infection reservoirs, manage transmission hotspots/
demands of malaria elimination requires further explo- pockets, as well as identify and treat imported infections
ration, validation and application; particularly in the before local transmission reoccurs [14]. These operational
context of resource-poor settings. priorities, coupled with the explicit spatial nature of ma-
laria itself, highlight the importance of geography and
Operational challenges facing malaria elimination from locale across all aspects of malaria elimination manage-
a geospatial perspective ment and stress the relevance of incorporating a spatial
Malaria elimination is back on the global health agenda [1– component into any accompanying HIS.
3]. Current global strategies for the elimination and even- The significance of mapping as a powerful epidemiologi-
tual eradication of malaria are outlined in the Roll Back cal tool has been recognised right from the foundations of
Malaria (RBM) Global Malaria Action Plan (GMAP) and epidemiology, when John Snow’s famous maps were pub-
include: (i) the scaling-up and sustainment of intensive lished relating the location of cholera cases to the Broad
malaria control operations; (ii) progressively eliminating Street water pump [15]. Current mapping approaches have
malaria from the endemic margins inward (i.e. shrinking largely focused on illustrating, modelling and predicting
the malaria map); and (iii) the continuation of research into the distribution or patterns of disease and disease risk.
new tools and approaches to malaria control and elimina- However, comparatively less research has been conducted
tion [4,5]. on how best to utilise and apply geospatial technology to
To support effective implementation of the GMAP, ro- actively support the priorities and increased demands of
bust health systems are essential in each endemic setting malaria elimination at an operational level, particularly in
because this ensures the effective delivery of scaled-up the context of resource-poor and capacity-limited settings.
malaria services and interventions at optimum levels of The aim of this paper is to provide an overview of GIS and
coverage in target areas [6,7]. As malaria programmes mapping as an operational tool for malaria management;
enter pre-elimination and progress forward, detailed and and highlight the potential expanded role of GIS-based
responsive surveillance also becomes a central component SDSSs in guiding and meeting the scaled-up operational
to any supporting health system [6,8]. To support the demands of current day malaria elimination programmes.
transition from control to elimination the need for im-
proved data and an ability to generate additional informa- Contemporary malaria mapping approaches using
tion, knowledge and evidence from such data are a geographical information systems
prerequisite [9]. This often presents significant challenges With the introduction and expansion of GIS today, the role
of mapping in malaria control and elimination has grown.
Malaria incidence or prevalence mapping is the most
Corresponding author: Kelly, G.C. ([email protected]).
basic contemporary application and is primarily used to
Keywords: malaria elimination; spatial decision support system; geographic
information systems; surveillance; monitoring and evaluation. visualise and identify trends and patterns in the spatial
1471-4922/$ – see front matter ß 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.pt.2012.04.002 Trends in Parasitology, July 2012, Vol. 28, No. 7 297
Review Trends in Parasitology July 2012, Vol. 28, No. 7
distribution of malaria over defined geographical areas distribution. GR involves data collection, mapping and
[16,17]. Mapping and geostatistical applications are used sampling procedures to determine the number, location
to identify relationships between the spatial distribution of and accessibility of settlements within intervention target
malaria and other variables such as weather and climate areas. Operationally, it provides a basis for the selection of
[18–23], land use and demography [24], and vector breed- field centres and depots, for designing schedules and itin-
ing sites [25,26]. Mapping of malaria risk is also now a eraries, planning deployment of transport and assessing
prominent form of contemporary mapping that utilises the completion of planned activities [48]. GR can also be
spatial data and imagery from a variety of remote sources used to define as accurately as possible the geographical
such as satellite imagery, aerial photography and radar. limits of malaria epidemics and transmission foci, and
Remote sensing (RS) data have been used widely for the assess epidemic potential [49,50].
identification, characterisation, monitoring and surveil- During the global malaria eradication period (GMEP)
lance of breeding habitats, and mapping of malaria risk of the 1950s to the 1960s, GR involved conducting de-
[27–32]. tailed paper-based censuses and surveys of all households
Advanced GIS-based spatial analysis techniques have within target areas, sampling and measuring selected
also been adopted to identify, predict and map malaria risk building structures and developing sketch maps of set-
at a variety of different scales. On a global scale, the tlements using compass and pacing techniques [48,51].
distribution of Plasmodium falciparum endemicity has Figure 1a provides an example GR household locality
been mapped from parasitological data using robust geos- sketch map produced during the GMEP era. A major
tatistical modelling frameworks [33,34]. Similar spatial limitation of traditional GR has often been the time-
statistical models have also been produced to explore consuming, inaccurate and resource-intensive processes
and predict malaria risk at regional, national and local associated with fieldwork and paper-based sketch-
levels [32,35–43]. From a malaria elimination perspective, mapping. Therefore, traditional approaches to GR are
predictive maps provide essential tools to illustrate malar- now often omitted from current day malaria programmes
ia risk and its distribution across a variety of scales (i.e. due to these inefficiencies.
globally, nationally and subnationally), highlight favour- During the GMEP, interventions were often carried out
able areas for elimination, and strategically target priority independently from existing health systems because they
interventions towards relevant foci within designated were considered inadequate to support the largely verti-
elimination zones. cally organised malaria elimination programmes [6]. Be-
Map-based graphical interfaces are also being integrat- cause technology at the time did not exist to integrate
ed into modern reporting applications. For example, mo- paper-based GR data into existing health systems, GR data
bile phone short message service (SMS) technology has generally had poor geographical coverage and were not
been utilised in Africa to strengthen the routine reporting suited to surveillance [14,52]. However, recent advance-
of antimalarial drug supplies at health facilities [44,45]. ments in handheld mobile technologies, such as personal
Coupled with the standard tabular reporting functionality, digital assistant (PDA) devices, tablet computing, smart-
data from health facilities sent via SMS are also automati- phones and integrated GPSs, now present significant
cally updated onto web-based map interfaces to visualise opportunities to expand the coverage and application of
the real-time supply and spatial distribution of antimalar- field-based GR. Handheld technologies available today can
ials within target areas, providing a mechanism to support support the rapid collection and subsequent immediate
drug supply management and the timely detection of stock- mapping of data at a high resolution (e.g. the household
outs at the health facility level [44,45]. level), providing a cost-effective, efficient and accurate
At a programme implementation level, GIS technology approach to geospatial field-based data collection [53–56].
is still only playing a limited role in supporting direct If consolidated effectively within a GIS, high-resolution
operational priorities of malaria elimination within desig- GR and micromapping data, particularly at a sub-
nated target areas. Until recently, little research has settlement level (e.g. household, community or similar),
explored the application of geospatial information and can provide a detailed georeferenced dataset to support key
GIS technology for malaria elimination operations, and operational priorities of elimination such as the implemen-
in many cases a lack of data at a high enough resolution to tation, monitoring and evaluation of targeted vector con-
support elimination activities has been a limiting factor trol interventions and surveillance [57]. Recent GR
[46]. activities carried out in designated elimination provinces
in the Solomon Islands and Vanuatu have adopted such a
Geographical reconnaissance: an operational tool for standardised approach to rapid GR and micromapping,
malaria elimination utilising handheld PDA, GPS and GIS technology to
Historically, the lack of access to spatial data and opera- develop a georeferenced database of households within
tional tools often represented a significant barrier in target elimination zones [57]. Through a GIS-based SDSS,
control operations [47]. Despite these traditional limita- this dataset has been used to guide priority elimination
tions, mapping has long played an important operational interventions such as focal IRS and universal long-lasting
role in malaria control and eradication. GR has been used insecticidal net (LLIN) distribution to ensure maximum
in malaria programmes to identify and map target areas and uniform levels of coverage are achieved in the desig-
and enumerate populations for the coordination, imple- nated target areas [58]. Figure 1b provides an example
mentation and quality control of field operations such LLIN coverage map automatically produced within the
as indoor residual spraying (IRS) and mosquito net SDSS based on household GR data to support scaled-up
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Review Trends in Parasitology July 2012, Vol. 28, No. 7
malaria elimination interventions in the Solomon Islands. a spatial or geographical focus. Figure 2 illustrates a
As the intensive control and progressive elimination pro- conceptual SDSS framework for malaria elimination.
grammes in the Solomon Islands and Vanuatu continue to Key elements of a SDSS include: (i) data inputs from a
evolve, this framework is anticipated to provide a location- variety of sources (including geospatial data layers); (ii)
based platform to support additional operational priorities automated outputs to guide informed and strategic deci-
including passive and targeted active case detection, sur- sion making for designated applications; (iii) application/
veillance and response. intervention outcomes re-entered back into the SDSS as a
cyclical input; and importantly (iv), expert knowledge,
From a spatial decision support system to malaria integrated throughout all stages of the SDSS process.
elimination Although only limited research has been conducted on
A SDSS provides computerised support for decision mak- the operational applications of SDSS for malaria elimina-
ing where there is a geographic or spatial component to a tion, elements of SDSS design have been implemented in
decision [59]. These are generally based around a GIS that several vector-borne disease control programmes to date
integrates database management systems with analytical (Box 1). A detailed overview of the current role of SDSS in
models, graphical map display and tabular reporting ca- supporting the progressive malaria elimination pro-
pabilities, and the expert knowledge of decision makers grammes in the Solomon Islands and Vanuatu is also
[59,60]. A SDSS provides a mechanism to link routinely presented in Box 2.
collected data with associated geographic locations, con-
duct spatial queries and analysis, and produce automated The transition from control to elimination
reports and illustrative maps for relevant areas of interest. As countries pursue GMAP [5] and progressively shift from
These systems, when designed and applied effectively, malaria control to pre-elimination, elimination and the
have the potential to provide health programmes with a eventual prevention of reintroduction of malaria, respec-
powerful and user friendly operational tool for evidence- tive programmes must not only meet the scaled-up opera-
based decision making to support management issues with tional priorities of these individual phases but also have
(a) (b)
Key: Household - with LLIN Household - without LLIN Unpopulated household Settlement name Health facility Road / track Watercourse
N
W E
S
0 100 200 Metres
TRENDS in Parasitology
Figure 1. GR mapping: traditional processes versus new technology. (a) An example of a hand-drawn GR household locality map produced during the GMEP using
traditional field-based sketching methods to support IRS interventions (circa 1961–1962) [48]. (b) An example of a LLIN household distribution coverage map automatically
generated in a SDSS, Temotu Province, Solomon Islands (2011). Household location data collected during baseline GR operations using digital handheld computer and
GPS technology is stored in a SDSS and used to support the planning, implementation and assessment of priority elimination interventions such as LLIN distribution and
focal IRS, and positive case surveillance at a household level as part of the progressive Solomon Islands Malaria Elimination Programme.
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Review Trends in Parasitology July 2012, Vol. 28, No. 7
Malaria elimination spatial decision support system conceptual framework
Routinely reported data Field surveys / data collection e.g. Passive case data; e.g. Geographical reconnaissance (GR); health information system (HIS) reports; larval surveys; routine vector control activities malaria indicator surveys (MIS)
Inputs Data / results re-entered into SDSS to provide continuous operational support
Baseline spatial / GIS data e.g. Topographic data layers Vector control and malaria prevention (coastline, rivers, terrain, etc.); Expert knowledge interventions demographic / infrastructure data e.g. Malaria / vector control staff; e.g. Targeted / Focal IRS; (settlements, roads, buildings, etc.) health facility workers; blanket LLIN Distribution; community volunteers entomological surveillance; Spatial decision community awareness Support system
Case management, surveillance- response Informed decision / e.g. Strategic active case detection; strategic action outbreak detection and response; (to support operational case investigation and follow-up; priorities) drug resistance monitoring
Outputs Monitoring and evaluation e.g. Assessment and mapping of malaria Tabular reports Statistical / spatial analysis intervention coverage and service delivery; e.g. Routine Health Facility reports, e.g. Geo-statistical analysis, modelling, malaria risk stratification; Automated summary reports (e.g. vector prediction and mapping of malaria inci- automated reporting and data analysis; control interventions, case reporting, etc.) dence, risk, and relationships strengthened malaria information systems (MIS) and elimination database
Graphical maps e.g. Positive case distribution maps; Applications Household / GR spot maps; Intervention coverage maps
TRENDS in Parasitology
Figure 2. A conceptual SDSS framework for malaria elimination. This schematic illustrates a conceptual SDSS design framework highlighting the inputs, outputs,
applications and relationships of key elements of a SDSS in the context of supporting the operational priorities and increased demands of malaria elimination and
intensified control.
the ability to effectively measure and assess progress to programmes to implement elimination, can be developed
support the strategic transition between phases. A stan- within a GIS-based SDSS to visualise (at differing admin-
dardised approach to stratifying both malaria risk and istrative and geographical scales) and delineate target
incidence, as well as the overall preparedness of malaria areas, and to measure and assess progress made within
Box 1. Example of SDSS applications in malaria and vector-borne disease management
A GIS-based malaria case surveillance system has been trialled by the operator, and implement remedial action when required to assure
Mpumalanga Malaria Control Programme in South Africa. Malaria high coverage and programme efficiency [66].
cases were georeferenced at town and village level and maps of Following the success of stand-alone DSS in malarious provinces of
malaria incidence generated [64]. These maps illustrated spatial South Africa, an integrated Malaria Information System (MIS) has
heterogeneity of malaria risk that had previously been concealed in been developed and implemented in this region to facilitate
table-based summary data on incidence, and were used to enhance pragmatic decision making [67,68]. Maps generated from the GIS-
decision making through the identification of priority areas and based MIS can be produced at a variety of administrative levels
efficient allocation of limited resources to support spraying interven- ranging from national to village level and have played an important
tions in these locations [64]. Similarly, a GIS-based DSS has been role in formulating malaria insecticide and drug policies, providing
developed in Dindigul, Tamil Nadu, India to guide control interven- appropriate information for tourists, evaluating changes in malaria
tions. A significant benefit of this SDSS was that it enabled real-time transmission over time and allocating resources to control malaria
monitoring via a graphical map interface of mosquito larval densities, [67].
malaria cases and community awareness programmes [65]. Surveil- SDSS applications have also been implemented in other vector-
lance carried out using the SDSS enabled programme managers to borne disease control programmes, such as for dengue. In Thailand,
identify localised clusters of malaria transmission and to rapidly vector populations and dengue cases have been mapped within a
associate probable cause, specific vectors and probable human SDSS framework and used to monitor dengue outbreaks [69].
source, so that appropriate preventative actions could be carried out Similarly, a SDSS has been implemented in Singapore to identify,
[65]. map and monitor dengue ‘hotspots’ [70]. Dengue vector surveillance
A computerised management system with a SDSS component has in Singapore is also carried out using GIS, integrating data from an
been implemented in southern Mozambique to monitor vector island-wide monitoring network of 2000 ovitraps [71]. Vector breeding
control spraying operations [66]. The number of structures sprayed data collected using the ovitraps are analysed weekly to identify
was digitally recorded, insecticide spray application rates calculated hotspots and risk areas, and results used to guide control operations
and coverage by a spray team mapped over a large geographical area [71]. A dengue SDSS has been implemented in Mexico utilising
2 TM
(approximately 220 000 structures, covering an area of 13 770 km ) Google Earth and other free software such as the WHO Health-
[66]. This SDSS provided programme mangers with an effective Mapper, highlighting the opportunities to strengthen overall public
operational tool to actively monitor resource usage and spray health capacity and facilitate SDSS approaches to the prevention and
progress, identify problems at the level of an individual spray control of vector-borne diseases in resource-poor environments [63].
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Box 2. Supporting Pacific island malaria elimination with spatial decision support systems
Progressive malaria elimination programmes are currently implemen- example screenshot of the Isabel Province SDSS graphical user
ted in Temotu and Isabel Provinces, Solomon Islands and Tafea interface, illustrating the map-based and tabular components of the
Province, Vanuatu. Key challenges faced in all three provinces are system. SDSS applications being developed in the Solomon Islands
characterised by those typical of remote Melanesian communities and and Vanuatu are now currently focusing on guiding geospatial case
include limited infrastructure, access and human resource capacity. As surveillance response as each respective programme prepares to move
a means to support the increased operational demands of elimination towards the latter stages of elimination.
and to ensure the effective targeting and delivery of interventions and Individual SDSS frameworks have been developed at each
essential services is achieved, particularly in the context of the local provincial level and are primarily operated by provincial VBDCP
challenges, a SDSS framework has been adopted (Figure 2). officers. A key development focus is the customisation of individual
As part of the transition from control to elimination, universal provincial SDSSs to support the specific needs, existing systems and
household LLIN distribution and focal IRS interventions were nomi- software, and current elimination position of each respective
nated as key frontline strategies. Routine malaria data and entomolo- province. Hardware used includes standard laptop computers for
gical surveys [72], as well as malaria risks maps in Tafea [43], were used SDSS operation and integrated PDA/GPS for field activities. Microsoft
to identify focal IRS areas. GR using integrated PDA and GPS was Access (Microsoft Corporation, Redmond, WA, USA) and MapInfo
carried out by provincial vector-borne diseases control programme Professional (Pitney Bowes Software Inc., Troy, NY, USA) software
(VBDCP) personnel to map households and collect baseline information are used as the SDSS platform.
required to support key interventions [57]. A total of 20 485 households As illustrated in Figure 2, expert knowledge is an integral
and a population of 90 292 were recorded during GR operations, with component of a SDSS framework. To ensure operational capacity
an average of 42.5 households and population of 180 recorded per PDA/ exists at each provincial level, field-based training is conducted before
GPS unit per day. As part of daily GR procedures automated data the implementation of any additional SDSS application. Collaboration
backup and manual data verification procedures were also conducted. and continuous communication is also prioritised, with a Pacific
GR household data have formed an integral baseline component of Malaria Elimination Surveillance, Monitoring and Evaluation (SM&E)
the SDSS approach to support elimination. Both graphical map-based Technical Working Group providing a forum for Solomon Islands and
and tabular data outputs were utilised to support all facets of managing Vanuatu national and provincial VBDCP SM&E personnel, and
and assessing priority interventions such as LLIN distribution, external technical partners, to exchange ideas, access remote support
entomological surveillance and focal IRS [58]. Figure 3 provides an and discuss SDSS issues.
these areas throughout all phases of intensified malaria powerful visual mechanism to monitor the distribution of
control and elimination. essential malaria services (such as antimalarial drug sup-
To support intensified control (and the future identifi- plies) and support the early detection of potential disrup-
cation of areas favourable for elimination), a SDSS ap- tions to their effective delivery, as well as provide an
proach to national malaria surveillance is currently in evidence-based tool to support effective decision making
development in the Solomon Islands and Vanuatu, expand- and guide appropriate response actions as required.
ing upon the application for elimination presented in
Box 2. Following a similar framework as illustrated in Surveillance response and prevention of reintroduction
Figure 2, this approach will: (i) integrate routinely National programmes progressing with malaria elimina-
reported HIS information to baseline GIS data (i.e. health tion must have the capacity to reorient themselves to
facility location and administrative boundaries including further emphasise surveillance, reporting and information
provincial and sub-provincial); to (ii) automate tabular, systems [5]. As the numbers of positive cases in target
map-based and geostatistical data outputs across varying areas fall and elimination priorities also shift towards
geographical scales on a scheduled basis; and (iii) empower prevention of reintroduction, national programmes will
local decision makers with data to guide the spatial target- require rigorous evidence-based surveillance strategies
ing of relevant intensified control interventions. and approaches to ensure (and report) that key elimination
interventions have effectively interrupted transmission
Supporting the operational priorities of elimination over a sustained period of time. The geographical nature
As indicated in Figure 2, an integrated SDSS also provides and spatial variability of malaria lends itself well to ele-
the potential to directly support several operational priori- ments of GIS and map-based monitoring tools. Scope cur-
ties of malaria elimination including: (i) routine reporting, rently exists to further develop and strengthen SDSS-
monitoring and evaluation; (ii) vector control and malaria based principles for malaria elimination to support: (i)
prevention interventions; and (iii) case management and the detailed surveillance of passively reported cases via
surveillance response. The ability to merge traditional field- a map-based interface to view the respective spatial–tem-
based mapping principles such as GR with GIS and data- poral distribution of individual malaria cases; (ii) individ-
base technology within a SDSS creates an effective platform ual malaria case investigation and follow-up; and (iii) the
for increased user access and functionality of spatial data to development of surveillance response mechanisms to stra-
support decision-making processes (Figure 3). A SDSS tegically guide active case detection activities based on the
framework can create an opportunity to integrate and relate spatial distribution of passive case data to detect, locate
traditional elements of malaria management with geospa- and halt local transmission, and prevent reintroduction in
tial data, potentially empowering local malaria personnel at designated elimination zones.
an operational level with necessary tools to support the
geographical focus required for the successful implementa- Technical and operational capacity
tion of key interventions within an elimination context. As described in Box 2, SDSS design should focus on
Similarly, the ability to integrate map-based functionality addressing the specific needs of an individual programme
into an elimination-focused health system could provide a and aim to incorporate and build upon existing systems
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TRENDS in Parasitology
Figure 3. Screenshot of Isabel Province, Solomon Islands SDSS. (a) Customised drop-down menu to access SDSS applications developed to support priority interventions
throughout all phases of the Isabel Province Elimination Programme including: the identification of target area and collection of baseline spatial data – ‘Isabel Household Map’
and ‘Geographical Reconnaissance’; intervention management: ‘LLIN Distribution’, ‘IRS’ and ‘Entomology’; and case management and prevention of reintroduction: ‘Malaria
Case Surveillance’. (b) Example of a SDSS dialog box illustrating key components of the IRS intervention management application. (c) Example of map-based graphical user
interface component of the SDSS to spatially monitor and access relevant data. (d) Example of the tabular component of the SDSS used to extract, analyse and export data.
and software in place. Access to GIS software has been a collaborative websites (e.g. internet forums and wikis),
significant obstacle in the past, with the cost of proprietary remote support options present a feasible, effective and
software often a limiting factor for health programmes cost-effective approach for sustaining operational capacity,
with limited resources. However, with the increasing particularly in remote settings.
availability of free open source GIS software, open-access
GIS data and web-based mapping alternatives (e.g. Quan- Current limitations and future research
tum GIS, OpenStreetMap and OpenLayers), it is expected Limited research has been conducted to quantify the ben-
that access to these resources will steadily improve efits of SDSS as a decision-support tool for malaria elimi-
[61–63]. nation, or to explore the limitations of the SDSS approach.
Local knowledge is an essential component of any Challenges faced by malaria programmes regarding GIS
decision support application for managing malaria inter- have included information technology concerns such as
ventions [8]. As suggested in Figure 3, expert knowledge is hardware, software and training; availability of adequate
central to an effective SDSS framework. In the context and reliable GIS data; and the accessibility of appropriate
of malaria elimination, the expert knowledge of local (and user friendly) GIS methodologies [16]. As mentioned,
programme personnel is paramount to the effective the increasing open-source market is likely to provide
implementation and delivery of any intervention or service. increased access to GIS software and baseline GIS data.
As such, SDSS design must be targeted, developed and GR using GPS and handheld digital devices also provides
implemented at the local level. As outlined in Box 2, empha- an effective field method for the rapid and accurate collec-
sis should be placed on building SDSS operational capacity tion of relevant georeferenced data. SDSS user acceptabil-
of local elimination officers through a combination of field- ity surveys have also been conducted in the Pacific,
based training and continuous remote support. Access to indicating strong support for SDSS methods for guiding
continuous remote support through the establishment of the operational priorities of malaria control and elimina-
forums such as technical working groups and web-based tion [57,58].
discussion boards is imperative to ensure operational ca- The small number of SDSS users and systems currently
pacity is sustained at the appropriate levels. Given current in place suggests a need for further research and broader
improvements in free web-based communication technology application of SDSS to validate the integrity and robust-
such as voice-over-Internet Protocol (VoIP) services and ness of these systems, particularly in resource- and
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304
Chapter 3
Modern geographical reconnaissance of target populations in malaria
elimination zones
3.1 Context
The capacity to collect, access and provide detailed and accurate data to generate useful
information, knowledge and evidence throughout all facets of program implementation is
essential for malaria programmes pursuing elimination. Following the presentation of a SDSS
framework for malaria elimination in Chapter 2, this chapter explores the application of
modern geographical reconnaissance (GR) using integrated personal digital assistant (PDA)
and global position system (GPS) handheld technology to define the spatial distribution of
target populations of malaria elimination zones and provide detailed baseline household and
demographic data for a malaria elimination SDSS.
Geographical reconnaissance involves census, mapping and sampling procedures to determine the quantity, location and accessibility of settlements within target areas.
Traditionally, GR has been used in malaria control to obtain relevant settlement data and
produce locality sketch maps to support the implementation and management of interventions
such as indoor residual spraying (IRS). Limitations associated with the time-consuming and
specialist nature of manual GR field operations, which include compass-based navigation and pacing, paper-based data collection and hand-drawn cartography, have impeded the ability of malaria programmes to efficiently collect quality baseline data. These manual techniques have also restricted the geographical scale at which GR can be conducted.
36
This chapter examines the application of modern geospatial technology to conduct GR in the malaria elimination provinces of Solomon Islands and Vanuatu to generate essential baseline data for the establishment of a GIS-based SDSS framework for malaria elimination; and to support long lasting insecticidal net (LLIN) distribution, focal IRS, and high-resolution surveillance and response.
Findings from this chapter are significant in relation to the current call for research into new tools and approaches to support elimination; and the operational need for malaria programmes to collect and access detailed and accurate data to generate useful information, knowledge and evidence throughout all facets of program implementation. This study demonstrates the effectiveness and efficiency of implementing GR using modern geospatial tools to rapidly generate essential baseline data for the establishment of a SDSS framework and to define the spatial distribution of target populations for malaria elimination.
3.2 Details of Publication
This chapter was published in the peer-reviewed journal Malaria Journal in 2010. Since publication, the manuscript has been listed as a BioMed Central “Highly Accessed” journal article.
Kelly GC, Hii J, Batarii W, Donald W, Hale E, Nausien J, Pontifex S, Vallely A,
Tanner M, Clements A: Modern geographical reconnaissance of target
populations in malaria elimination zones. Malaria Journal 2010, 9:289
37 Abstract
Background: Geographical Reconnaissance (GR) operations using Personal Digital
Assistants (PDAs) and Global Positioning Systems (GPS) have been conducted in the
elimination provinces of Temotu, Solomon Islands and Tafea, Republic of Vanuatu.
These operations aimed to examine modern approaches to GR to define the spatial
distribution of target populations to support contemporary malaria elimination
interventions.
Methods: Three GR surveys were carried out covering the outer islands of Temotu
Province (October - November, 2008); Santa Cruz Island, Temotu Province (February
2009) and Tanna Island, Tafea Province (July - September 2009). Integrated
PDA/GPS handheld units were used in the field to rapidly map and enumerate
households, and collect associated population and household structure data to support
priority elimination interventions, including bed net distribution, indoor residual
spraying (IRS) and malaria case surveillance. Data were uploaded and analysed in
customized Geographic Information System (GIS) databases to produce household
distribution maps and generate relevant summary information pertaining to the GR
operations. Following completion of field operations, group discussions were also
conducted to review GR approaches and technology implemented.
Results: 10,459 households were geo-referenced and mapped. A population of 43,497
and 30,663 household structures were recorded during the three GR surveys. The
spatial distribution of the population was concentrated in coastal village clusters.
Survey operations were completed over a combined total of 77 field days covering a
total land mass area of approximately 1103.2 km2. An average of 45 households, 118
38 structures and a population of 184 people were recorded per handheld device per day.
Geo-spatial household distribution maps were also produced immediately following
the completion of GR fieldwork. An overall high acceptability of modern GR
techniques and technology was observed by both field operations staff and
communities.
Conclusion: GR implemented using modern techniques has provided an effective and
efficient operational tool for rapidly defining the spatial distribution of target
populations in designated malaria elimination zones in Solomon Islands and Vanuatu.
The data generated are being used for the strategic implementation and scaling-up of
priority interventions, and will be essential for establishing future surveillance using
spatial decision support systems.
URL: http://www.malariajournal.com/content/9/1/289
3.3 Supplementary Information
Additional materials included in the published manuscript are presented in the thesis as
Appendices B and C.
39 Kelly et al. Malaria Journal 2010, 9:289 http://www.malariajournal.com/content/9/1/289
RESEARCH Open Access Modern geographical reconnaissance of target populations in malaria elimination zones Gerard C Kelly1*, Jeffrey Hii2, William Batarii3, Wesley Donald4, Erick Hale3, Johnny Nausien4, Scott Pontifex5, Andrew Vallely1, Marcel Tanner6, Archie Clements1
Abstract Background: Geographical Reconnaissance (GR) operations using Personal Digital Assistants (PDAs) and Global Positioning Systems (GPS) have been conducted in the elimination provinces of Temotu, Solomon Islands and Tafea, Republic of Vanuatu. These operations aimed to examine modern approaches to GR to define the spatial distribution of target populations to support contemporary malaria elimination interventions. Methods: Three GR surveys were carried out covering the outer islands of Temotu Province (October - November, 2008); Santa Cruz Island, Temotu Province (February 2009) and Tanna Island, Tafea Province (July - September 2009). Integrated PDA/GPS handheld units were used in the field to rapidly map and enumerate households, and collect associated population and household structure data to support priority elimination interventions, including bed net distribution, indoor residual spraying (IRS) and malaria case surveillance. Data were uploaded and analysed in customized Geographic Information System (GIS) databases to produce household distribution maps and generate relevant summary information pertaining to the GR operations. Following completion of field operations, group discussions were also conducted to review GR approaches and technology implemented. Results: 10,459 households were geo-referenced and mapped. A population of 43,497 and 30,663 household structures were recorded during the three GR surveys. The spatial distribution of the population was concentrated in coastal village clusters. Survey operations were completed over a combined total of 77 field days covering a total land mass area of approximately 1103.2 km2. An average of 45 households, 118 structures and a population of 184 people were recorded per handheld device per day. Geo-spatial household distribution maps were also produced immediately following the completion of GR fieldwork. An overall high acceptability of modern GR techniques and technology was observed by both field operations staff and communities. Conclusion: GR implemented using modern techniques has provided an effective and efficient operational tool for rapidly defining the spatial distribution of target populations in designated malaria elimination zones in Solomon Islands and Vanuatu. The data generated are being used for the strategic implementation and scaling-up of priority interventions, and will be essential for establishing future surveillance using spatial decision support systems.
Background areas for malaria elimination and intensified malaria con- Following recent international attention, renewed politi- trol [2]. As part of this increased focus, current strategies cal commitment and dedication of resources, the concept for the reduction of the global burden of malaria and the of malaria elimination and a refocusing of intensive eventual elimination of the disease have been outlined in malaria control is now back on the world agenda [1]. the Roll Back Malaria (RBM) Global Malaria Action Plan Recent global malaria maps have illustrated patterns of (GMAP). These include: the scaling up and sustainment worldwide malaria endemicity, highlighting opportunistic of intensive malaria control operations; progressively eliminating malaria from the endemic margins inward * Correspondence: [email protected] (i.e. shrinking the malaria map); and the continuation of 1Pacific Malaria Initiative Support Centre, Australian Centre for International research into new tools and approaches to malaria con- and Tropical Health, School of Population Health, University of Queensland, trol and elimination [3,4]. Brisbane, Australia Full list of author information is available at the end of the article
© 2010 Kelly et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Kelly et al. Malaria Journal 2010, 9:289 Page 2 of 12 http://www.malariajournal.com/content/9/1/289
To achieve the desired outcomes of the GMAP, it is Development (AusAID). This programme aims to sup- essential that all of these strategies proceed concurrently port the National Vector Borne Disease Control Pro- [3]. As malaria control operations intensify and an grammes (NVBDCP) of the two countries to scale up increasing number of national programmes move intensified control nationwide and to eliminate malaria towards the goal of elimination, the scaling-up of inter- in selected provinces. The main interventions are uni- ventions, such as indoor residual spraying (IRS) and versal household LLIN distribution and focal IRS. Elimi- long-lasting insecticidal bed nets (LLIN), are essential. nation activities have commenced in Temotu Province Similarly, as these interventions are rolled out, so will in Solomon Islands and Tafea Province in Vanuatu [26]. the need to adopt effective and practical operational To date, little research has addressed the approach, tools for the coordination, monitoring and surveillance relevance and feasibility of GR within current-day of key activities within target areas. malaria elimination frameworks. The aims of this study Geographical Reconnaissance (GR) involves census, were to examine a modern approach to GR using digital mapping and sampling procedures to determine the geospatial survey technology and explore its application quantity, location and accessibility of settlements within in contemporary malaria elimination programmes. To target areas. It provides the basis for the selection of support this study, GR operations were conducted in field centres and depots, for designing schedules and the elimination provinces of the Solomon Islands and itineraries of operations, planning deployment of trans- Vanuatu. Additional aims of these operations were to port, and assessing completion of planned activities [5]. define and quantify the target populations and house- GR has traditionally been used in malaria control and hold structures; to provide a framework for the plan- eradication programmes to identify target areas and ning, implementation and monitoring of nominated enumerate populations for the coordination, implemen- elimination interventions; and to establish a foundation tation and quality control of operations. During the from which future malaria elimination surveillance activ- malaria eradication era of the 1950-60s, GR involved ities can be carried out in these remote communities. conducting detailed paper-based censuses and surveys of all households; sampling and measuring selected build- Methods ing structures; and developing locality-sketch maps of Survey area(s) settlements using compass and pacing techniques [5,6]. Baseline GR operations were conducted in the malaria GR can also be used to define as accurately as possible elimination provinces of Solomon Islands and Vanuatu the geographical limits of malaria epidemics especially (Figure 1). Two separate surveys were carried out in foci and assess epidemic potential [7,8]. Temotu Province, Solomon Islands, covering all islands Traditional GR operations required specialist skills, except the remote Polynesian islands of Tikopia and training and equipment in survey, navigation and carto- Anuta, which are known to be malaria-free. Operations graphy [5,6]. As such, a major disadvantage of tradi- were conducted in the outer islands of Temotu from tional paper-based GR has been the time-consuming October-November 2008 (GR Operation 1), and on the and resource-intensive processes involved, often limiting main island of Santa Cruz during February 2009 (GR the willingness of programme managers to undertake Operation 2). In Vanuatu, GR operations were con- such activities. However, with the growing availability of ducted in the previously designated focal coastal IRS spatial data and access to geographic information system zone and northern health zone 2 of Tanna, Tafea Pro- (GIS) technology, the applicability of mapping and spa- vince, from July-September 2009 (GR Operation 3). tial analysis in vector-borne disease management is now Approval to conduct GR activities was provided by the well established [9-20]. The use of Personal Digital Ministries of Health in each country, who formally Assistants (PDAs) and Global Positioning Systems (GPS) requested support for these activities through in-country in the field has proven to be an accurate and cost- technical and development partners (i.e. WHO, Pac- effective approach to the collection and geo-positioning MISC). Fieldwork was conducted by NVBDCP staff, of data at a household level [21-25]. Through the com- with technical assistance from WHO and PacMISC. bination of baseline spatial data and integrated handheld PDA/GPS technology, GR can now be implemented Development of spatial data collection systems more efficiently. Selection of hardware and software was based upon the To support the global elimination strategy of shrinking existing government systems and operational capacity the malaria map from the endemic margins inward [3], within Solomon Islands and Vanuatu; practicality and progressive elimination programmes in Solomon Islands durability of equipment in the field; and maximizing and Vanuatu have been implemented with support from integration across all levels of data collection, manage- the Pacific Malaria Initiative (PacMI) programme, ment and analysis. Hardware included Trimble Juno ST funded by the Australian Agency for International (Trimble Navigation Limited, Sunnyvale, CA) handheld Kelly et al. Malaria Journal 2010, 9:289 Page 3 of 12 http://www.malariajournal.com/content/9/1/289
Figure 1 General Location map of Geographical Reconnaissance Operation Areas.
PDA units with integrated GPS and Durabook (Gamma- GIS-based ArcPad maps. These were used to provide a Tech Computer Corp., Fremont, CA) notebook compu- base from which households could be rapidly mapped, ters for data management and daily backup. ArcPad 7.0 surveyed and viewed in the field in relation to surround- (ESRI, Redlands, CA) software was used for data collec- ing features such as watercourses, coastlines, and local tion and field mapping operations, with post-fieldwork infrastructure. data storage, backup and analysis using both Microsoft Digital data collection forms (Figure 2) were developed Access (Microsoft Corporation, Redmond, WA) and in ArcPad in consultation with relevant stakeholders. MapInfo Professional 8.0 (Pitney Bowes Software Inc., These forms contained the following information: Troy, NY). Otterbox casings were also used in the field to provide a protective cover for the handheld PDA/ i) Household identification and enumeration GPS units. Handheld PDA/GPS devices, accompanying ▪ A unique identification number for every software and protective casing were purchased prior to household the GR operations, each collectively costing approxi- ▪ Village name and name of the head of the mately $1500 per unit. All other hardware and software household. was sourced from existing Ministry of Health resources. ii) The number, gender and ages of individuals resid- Collaborative agreements were developed between the ing in each household NVBDCP and relevant Government GIS data custodians iii) The number and types of structures (sleeping (i.e. the Ministry of Lands in both countries) to gain houses, kitchens, storage sheds, etc.) per household access and share data between the Ministries. These iv) The number of mosquito nets currently in the data were then adapted to produce standardized baseline household, and the number and sizes of new LLINs topographic maps. To aid general reconnaissance distributed (if relevant). throughout the household surveys, these maps were v) The geographical coordinates of the household uploaded onto the handheld PDA units as underlying (Projection: Longitude/Latitude WGS 84) Kelly et al. Malaria Journal 2010, 9:289 Page 4 of 12 http://www.malariajournal.com/content/9/1/289
Figure 2 Arcpad 7.0 Digital Data Collection Form developed for Geographical Reconnaissance Operations.
Automated numbers, checkboxes and drop-down minimising human resources, by keeping field teams menus were used in the design of the digital data collec- small and utilising community volunteers whenever pos- tion form, to simplify and standardize data entry proce- sible. Six VBDCP field officers participated in the survey dures. Separate forms were developed for Solomon in the outer islands of Temotu Province, Solomon Islands and Vanuatu due different traditional household Islands, eight in the Santa Cruz, Temotu Province sur- structure types being dominant in these island groups. vey,andfiveintheTannaIsland,TafeaProvince, A customized GIS application was developed using Vanuatu survey. VBDCP officers participating in opera- MapBasic (Pitney Bowes Software Inc., Troy, NY) (Figure tions had no initial mapping or GR experience and were 3) to enable automatic backup of data from multiple hand- sourced from both national and provincial levels held units into a central GIS data file stored on the field depending on the availability of personnel. In each field laptop, and to assist in daily data reviews, produce auto- operation, survey teams were divided into individual mated interactive household distribution maps and gener- survey mapping units, consisting of 2-3 individuals per ate survey summaries to keep track of progress in the field. unit (including community volunteers). Each mapping unit operated one handheld PDA and divided digital Planning, logistics and training data entry, household mapping and household enumera- Meetings were conducted with relevant NVBDCP and tion activities between the team. Field supervisors were community stakeholders prior to the implementation of also selected from the survey teams to be responsible GR to identify target areas, develop operational timelines for daily data backup, quality assurance and GPS data and assist in resource allocation. Priority was placed on monitoring, and electrical charging of PDAs. Teams Kelly et al. Malaria Journal 2010, 9:289 Page 5 of 12 http://www.malariajournal.com/content/9/1/289
Figure 3 Snapshot of automated GIS mapping application for Geographical Reconnaissance Operations. were supplied with handheld PDA/GPS units (including Cards were distributed as part of the household enu- spare batteries), a field laptop computer, small printer, meration component to provide a hard-copy record of stationary and a portable generator. Prior to the com- the household identity for future interventions. Survey mencement of each survey, workshops were conducted teams (usually the community volunteer) also painted in Temotu and Tanna to train participating VBDCP per- theuniquehouseholdnumberonthetopright-hand sonnel in the use of handheld PDA/GPS units. Addi- side of the household door or the nearest possible alter- tional training was also provided for field supervisors on native. No issues with compliance associated with this daily data backup and quality assurance, data security, activity were observed. Other structures (e.g. schools, PDA troubleshooting, and mobile mapping and sum- churches and garden houses) were also mapped and mary analysis techniques. enumerated as part of the survey and recorded as unoc- cupied structures. Data collection and household mapping surveys Following the completion of daily household survey A standardized approach was applied across all three and mapping operations, PDA data were backed-up surveys. Following deployment to designated operation automatically onto the field laptop computers using the areas, field teams met with local community leaders to customised GIS application. Following daily data backup inform the community about the rationale of the survey. procedures, household distribution maps and survey Local volunteers were also recruited from each village to summaries were also automatically updated using the act as guides and assist each unit in the household sur- customized GIS application to provide an overview of veys. These volunteers served to increase community household distribution within the survey area and detail- engagement, assist in language translation (when ing daily progress. Household data quality assurance required), and provide local knowledge. checks were also conducted by the field supervisor as Because blanket LLIN distribution and focal IRS within part of the daily backup process to identify and remedy each of these nominated target areas is to be implemen- obvious digital data entry or GPS errors. ted and monitored at the household level, the household All household data were stored in central GIS-enabled was used as the primary unit for GR. Households in Mel- databases located at the relevant national and provincial anesian countries are often associated with multiple sepa- vector borne disease control programme offices of the rate structures including traditional sleeping houses, elimination target areas. Data were automatically outdoor kitchens, resting shelters and storage sheds. To updated using the customized GIS application to link standardize GR operations, GPS coordinates were taken village, health operations zone, island, ward, and pro- adjacent to the main sleeping house of each household. vince names to each household unit. Using simple GIS All other associated household structures were then iden- techniques, descriptive maps of household distribution tified, counted and recorded. and summary statistics were then generated for Kelly et al. Malaria Journal 2010, 9:289 Page 6 of 12 http://www.malariajournal.com/content/9/1/289
elimination areas when required. Maps and household Temotu was non-existent and access to most settle- listings were printed in the field and given to local ments was limited to the sea and/or walking trails. The health facilities when relevant, providing a detailed over- highest populations in the outer islands were recorded view of the spatial distribution of their health catchment on the low-lying Reef Islands and concentrated in populations. coastal village clusters on the main islands of Ngawa, Ngalo and Fenualoa. Populations in Utupua, Vanikolo Assessing population at risk and Duff Islands were limited to patchy coastal villages, GR data were overlayed onto existing malaria risk maps with no settlements in the inland mountainous regions. produced for Tanna, Tafea Province [27], to estimate Population distribution on the most populous Temotu populations residing in areas of above-average malaria Province island group of Santa Cruz followed a similar prevalence. Overall, malaria prevalence on Tanna is esti- pattern to the outer islands, with settlement clusters mated at 2.2% for Plasmodium vivax and 1.0% for Plas- concentrated on the coastal fringes. However, settle- modium falciparum [27]. Locations where malaria ments and household structures were also located prevalence was above these respective overall prevalence within the low-lying inland regions of West Santa Cruz, rates were defined as having above-average risk. House- where road infrastructure currently exists. Figure 4 pro- holds with a probability greater than 50% of having vides an example island household distribution map above-average risk were then identified and associated produced as part of the Temotu Province, Solomon population and household structure data extracted. Islands GR operations. Population and household data were also summarized The population of the Tanna GR survey area in Tafea for the designated coastal IRS zone, covering all house- Province, Vanuatu, had greater spatial dispersion but holds located within 2 km of the coastline of Tanna. was concentrated in coastal regions and highland pla- teaus (Figure 5). A more extensive road network also Assessment of modern geographical reconnaissance currently exists on Tanna in comparison to Temotu approach, technology and acceptability Province in the Solomon Islands. Post-fieldwork interviews and debrief sessions where Unoccupied structures, generally associated with agri- conducted with all participating VBDCP field officers culture (e.g. copra dryers, garden and storage sheds), and provincial managers following the completion of were recorded throughout the study areas. Whilst not each GR operation. Informal sessions were held to ask permanently inhabited, these structures are still consid- participants about their individual perceptions of the GR ered significant, particularly in target areas identified for procedures applied and associated PDA/GPS handheld IRS, because people are known to sleep in them on an equipment used in the field, and the relevance of this intermittent basis. fieldwork for future elimination interventions. Partici- Figure 6 provides an example of a village household pants were also asked to give insight on how to improve map produced in the field and presented to health facil- field operations using modern survey equipment. Group ity officers in the remote outer islands of Temotu Pro- discussions were also held to provide feedback into the vince. (Note: Additional population distribution and future improvements and potential applications of these operational maps can be made available upon request to techniques relevant to the malaria programme. the corresponding author)
Results Efficiency of geographical reconnaissance field Baseline data collection summary procedures A total of 10,459 households were geo-referenced and The three GR operations covered a land mass area of mapped across the two countries. Data were recorded approximately 1103.2 km2 and a total area of approxi- on a total of 30,663 household structures and a popula- mately 30,904.5 km2 (including oceans). A total of 77 field tion of 43,497. Table 1 provides a breakdown of the sur- days were required to complete operations for all three veydatabyoperationzone.Detailedbreakdownsof surveys, with an average of 45 households, 118 household population by age and gender (see Additional file 1: GR structures and a population of 184 people recorded per population summary), and household structures by type PDA per day. Table 2 provides an overview of field proce- (see Additional file 2: GR structures summary) are also dures by individual GR operation. Average mapping times provided. between each GR operation varied, with factors such as access, terrain, technical difficulties and level of field General population and household distribution supervision all likely to have impacted field operations The population of Temotu Province, Solomon Islands time. Technical difficulties arose in GR operation 2 - was concentrated in coastal village clusters. With the Santa Cruz, Temotu Province, where one PDA malfunc- exception of West Santa Cruz, road infrastructure in tioned in a remote region and could not be repaired for Kelly et al. Malaria Journal 2010, 9:289 Page 7 of 12 http://www.malariajournal.com/content/9/1/289
Table 1 Summary of data collected during Solomon Islands and Vanuatu Geographical Reconnaissance operations, 2009 GR Operation Operation Zone Total Population Total Populated Households Total Households Total Structures GR1:Outer Islands, Temotu Province Duff Islands 556 118 158 399 Reef Islands 5958 1179 1468 3746 Utupua 1300 259 330 770 Vanikolo 1602 308 399 938 GR1: Total 9416 1864 2355 5853 GR2: Santa Cruz, Temotu Province Santa Cruz East 3950 717 881 1873 Santa Cruz West 8162 1563 1970 4046 GR2: Total 12112 2280 2851 5919 GR3: Tanna IRS Zone, Tafea Province Health Zone 1 5363 1064 1283 5214 Health Zone 2 9395 1863 2196 7299 Health Zone 3 3096 653 773 2923 Health Zone 4 4115 866 1001 3455 GR3: Total 21969 4446 5253 18891 three days, resulting in a moderate reduction of productiv- automatic backup procedures, with all team supervisors ity in this area over this period. Some minor technical dif- reporting favourably on all aspects of the daily automatic ficulties were reported during GR operation 3 - Tanna IRS back-up, map verification and digital data entry checking Zone, Tafea Province. However, these were dealt with by procedures. the field-teams independently within one day following standardised trouble-shooting methods covered during Acceptability of modern geographical reconnaissance initial training operations. No significant problems were approaches and technology recorded with GPS accuracy or digital data entry. No pro- An overwhelmingly positive response to the modern blems or data losses were also reported during the daily GR approach and technology was expressed by both
Figure 4 Santa Cruz, Temotu Province, Solomon Islands Population Distribution Map. Kelly et al. Malaria Journal 2010, 9:289 Page 8 of 12 http://www.malariajournal.com/content/9/1/289
Figure 5 Tanna GR Operation Zone, Tafea Province, Vanuatu Population Distribution Map.
VBDCP field officers and provincial managers during A high degree of community acceptability of modern post-operation interviews and group discussion. In par- mapping techniques was also observed in the field, with ticular, the time-saving benefits of no post-field work many community members taking a keen interest in the data entry requirements, the ability to map and visua- handheld units and associated GPS components during lise the spatial distribution of household data automa- demonstrations in the field. tically, and the rapid generation of summary data was noted by provincial managers and information officers. The population at risk Interest for applying GR techniques and mapping tech- Based on the Tanna Island risk maps, it was estimated nology for other components of malaria field work that a population of 2,070 are residing in areas of such as entomological surveys, and for the expansion above-average P. falciparum malaria prevalence, repre- of these procedures into other malaria control pro- senting approximately 9.4% of the surveyed population. vinces was also expressed. Additionally, the application A total of 412 populated households, and 1,727 spray- of these techniques and technology to support other able structures were estimated to be located in these facets of provincial and regional health services was focal areas. Similarly, it was estimated a total population noted. of 3,782 reside in focal areas of above average P. vivax Kelly et al. Malaria Journal 2010, 9:289 Page 9 of 12 http://www.malariajournal.com/content/9/1/289
Figure 6 Example Village Household Distribution Map. risk, representing 17.2% of the surveyed population. 713 Modern GR approaches and technologies were used to populated households and 2,953 spray-able structures develop rapid and accurate field-based procedures for were also located in these areas. A total population of the collection, spatial definition and mapping of malaria 13,818 and 12,453 spray-able structures were recorded elimination target populations. As these survey areas are in the designated coastal IRS intervention zone on located in remote Pacific islands, an emphasis was Tanna, geo-referenced to a total of 3,365 individual placed upon minimising the external resources (includ- household units. ing human) required to undertake such operations and empowering local vector control programmes by intro- Discussion ducing effective, user-friendly tools to support and effi- To guide elimination interventions in these regions the ciently guide the increased operational demands NVBDCP of both countries highlighted a need for the associated with malaria elimination. collection of reliable, accurate and programme-specific In the context of the target elimination areas, the data at a household level; particularly as relevant geo- coastal concentration and isolation of populations with- spatial census data was unavailable at the level of preci- out roads and reliable infrastructure, transportation and sion required for the designated target elimination areas. access are likely to be limiting factors to the success of
Table 2 Solomon Islands and Vanuatu Geographical Reconnaissance field procedures summary, 2009 GR Approx Number Survey Average Average Average Technical Technical Operation Land of PDA/ Days Households Structures Population Difficulties Field Area GPS units Mapped (per PDA Recorded (per Recorded (per during Supervision (km2) per Day) PDA per Day) PDA per Day) Operation provided GR1:Outer 79.63 2 19 61.97 154.03 247.92 No Yes Islands, Temotu Province GR2: Santa 546.6 4 18 39.6 82.21 167.9 Moderate No Cruz, Temotu Province GR3: Tanna 476.97 4 40 32.83 116.28 136.74 Minor Partial IRS Zone, Tafea Province Kelly et al. Malaria Journal 2010, 9:289 Page 10 of 12 http://www.malariajournal.com/content/9/1/289
elimination. Sound planning will be essential to ensure an influence on the high level of community acceptabil- interventions are efficiently and effectively carried out ity and compliance observed in the field, emphasising across all target areas. Data compiled and collected dur- the importance of building local programme capacity to ing GR activities will enable programme managers to independently drive contemporary intervention strate- visualize the spatial distribution of populations to assist gies and approaches. Ethical approval was not sought in the delineation of operational zones, the development forGRoperationsastheywereconsideredroutine of intervention timelines, and identifying transportation operational activities of the national malaria pro- routes and access strategies. Relevant data pertaining to grammes in both countries, with all collected data man- these priority interventions such as populations requir- aged as per confidentiality requirements of the ing LLINs and the total number of spray-able structures respective Ministries of Health. within focal IRS zones will assist in the accurate alloca- Whilst some data entry errors are expected as a result tion of resources to designated operational zones. of the digital data entry at the point of collection Household checklists and maps will also provide field approach, previous research suggests such errors are officers with a detailed mechanism for conducting and minimal when compared to traditional paper-based sur- monitoring priority interventions in target areas to veys [22,24,25]. The cost-benefit of adopting handheld ensure maximum coverage is achieved. Reporting will technology for field-based data collection has also been also be enhanced as the progress and coverage of inter- well established in earlier previous large-scale field ventions can be mapped and visualized at the household research [21,22,24,25]. Relatively high-end handheld level. units were purchased prior to the implementation of Population and household structure results from GR these surveys with the anticipation of being used operations carried out in the respective elimination throughout all facets of elimination interventions. Whilst zones illustrate the significant amount of detailed data these specific units were somewhat expensive, they have that can be collected over large geographical areas by been considered a viable investment as part of the small teams and within short timeframes, highlighting respective national malaria programmes to support all the efficiency of modern approaches to GR. Whilst routine field operations, mapping and data collection. there is an increased need for pre-operation training With the growing availability of mapping and data col- when using the handheld units for data collection [21], lection software (including freeware and open source), the time and opportunity costs associated with training as well as handheld and mobile phone technology cap- have been seen as valuable investments in building able of running GPS and data collection applications, human resource capacity within the national malaria alternative affordable technologies are also accessible to programmes of both countries. It is expected that as the a wider market looking to implement GR principles. technical competencies and experiences of field officers Additional benefits such as the increased accuracy and in the operation of these digital handheld devices resolution of GR data collected, and the immediate increases, the lag time associated with troubleshooting availability of summary information and maps relevant technical difficulties in the field will be reduced. Since for priority elimination interventions also make inte- the completion of these initial GR operations, additional grated PDA/GPS handheld technologies favourable; PDA/GPS based surveys have now been independently overcoming constraining issues associated with tradi- conducted by VBDCP officers in these regions and tional paper-based methodologies including low accu- expanding into other provinces, reflecting the high racy, and slow transaction times to verify data and acceptability, willingness and capacity of these pro- prepare operational maps following fieldwork. The high grammes to now implement GR operations using these portability of handheld PDA technology also provided modern techniques and technologies. considerable benefits in remote and difficult terrain Observations from the respective GR operations indi- where access and transportation posed significant logis- cate a high willingness and capacity of VBDCP field tical challenges. staff to adopt and successfully utilize modern mobile In addition to the traditional applications of defining mapping technology following initial basic training and target populations and providing operational support, technical guidance. The adoption of a simple custo- GR also provides an effective mechanism for further mized GIS-based application to automate data back-up strengthening current-day priority monitoring and eva- and mapping updates on a daily basis has also provided luation interventions such as detailed surveillance and a rapid and effective mechanism for VBDCP staff to case investigation. Malaria elimination requires robust monitor progress and edit data easily in the field. As and efficient surveillance mechanisms with full geogra- household mapping and PDA data entry operations phical coverage of target areas [28]. Active surveillance were lead by VBDCP staff well known within their of high-prevalence foci is also essential for successfully respective target communities, it is also likely this had interrupting malaria transmission [29]. The application Kelly et al. Malaria Journal 2010, 9:289 Page 11 of 12 http://www.malariajournal.com/content/9/1/289
of modern geo-spatial technology for GR can empower Additional material local programmes to carry-out detailed mapping and data collection operations in target areas efficiently and Additional file 1: Demographic description of the population of accurately. Data collected during the GR operations now Solomon Islands and Vanuatu Geographical Reconnaissance provides a spatial framework to not only guide key operation areas, 2009. Data provides a detailed demographic description by age and gender of the population recorded within each interventions such as LLIN distribution and IRS, but geographical reconnaissance operation area also carry-out surveillance and investigation at a house- Additional file 2: Household structures summary table of Solomon hold level across entire populations living in elimination Islands and Vanuatu Geographical Reconnaissance operation areas, areas. 2009. Data provides a detailed breakdown of the number of structures recorded by type within each individual geographical reconnaissance When coupled with additional tools such as malaria operation area. risk maps, entomology and mobility data, and local and historical knowledge of malaria transmission, stra- tegic active surveillance can also be targeted and prior- Acknowledgements itized in key focal locations following GR operations. It We would like to thank the hard work of the individuals from the Solomon is anticipated that the compilation of such data will Islands and Vanuatu National Vector Borne Disease Control Programs who provide the foundation for the establishment and assisted with the GR fieldwork, including Andrew Newa, Luke Osimane, Carlwyn Tengemoana, Manasseh Haridi and Cal Jere from the Solomon expansion of spatial decision support systems (SDSS) Islands; and Rueban Sumaki, James Amon, Aaron Tebi, Kevin Marafi and Tom in these elimination provinces. A SDSS developed from Iavis from the Vanuatu fieldwork teams. Additionally, we acknowledge the the ground up, offers the potential to provide a user- NVBDCP Directors Albino Bobogare (Solomon Islands) and George Taleo (Vanuatu), and provincial VBDCP supervisors Robert Raoga (Temotu) and friendly tool to equip locally-based programmes in Harry Iata (Tafea) for their enthusiasm and support in trialling these meeting the demands associated with the scaling-up of techniques in their respective jurisdictions. Finally, we thank the volunteers interventions and surveillance operations in malaria and communities of Temotu and Tafea provinces for their contribution, cooperation and overwhelming support in making these operations the elimination as well as intensified malaria control success they have been. regions. A.C.A.C. is supported by a Career Development Award from the Australian National Health and Medical Research Council (#631619). Conclusion Author details This study demonstrated a contemporary approach to 1Pacific Malaria Initiative Support Centre, Australian Centre for International GR to spatially define and enumerate target populations and Tropical Health, School of Population Health, University of Queensland, Brisbane, Australia. 2Malaria, Other Vectorborne and Parasitic Diseases, using modern geospatial survey technology. The focus Regional Office for the Western Pacific, World Health Organization, San was building inter-departmental capacity to indepen- Lazaro Hospital Compound, Manila, Philippines. 3National Vector Borne dently carry-out GR operations to support priority inter- Disease Control Programme, Ministry of Health, Honiara, Solomon Islands. 4National Vector Borne Disease Control Programme, Ministry of Health, Port ventions in the malaria elimination provinces in Vila, Republic of Vanuatu. 5Statistics and Demography Programme, Solomon Islands and Vanuatu. This approach has pro- Secretariat of the Pacific Community, Noumea, New Caledonia. 6Swiss vided an effective mechanism for rapidly and accurately Tropical & Public Health Institute, 4002 Basel, Switzerland & University of defining target populations where these interventions Basel, Basel, Switzerland. have been scheduled to take place. Data collected and Authors’ contributions modern techniques adopted during GR have empowered Authors that participated in the conception of the study design were GK and JH. Field training was conducted by GK. Field research operations were local VBDCP programmes with effective decision mak- coordinated by EH, GK, JN, WB, & WD. SP provided technical guidance and ing and operational tools to coordinate and monitor support. ACAC provided guidance on the scientific aspects of the study and scaled-up elimination interventions with the goal of provided detailed commentary on manuscript drafts. Additional scientific and technical guidance was provided by AV, JH & MT. Manuscript drafting ensuring universal coverage, and providing a foundation was carried out by GK with support from all authors. All authors read and for future surveillance activities. Following the high approved the final manuscript. acceptability and success of these initial GR operations, Competing interests both the Solomon Islands and the Republic of Vanuatu The authors declare that they have no competing interests. Vector Borne Disease Control Programmes are now dedicated to the expansion and further refinement of Received: 16 July 2010 Accepted: 20 October 2010 GR using modern mapping techniques and integrated Published: 20 October 2010
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Chapter 4
A spatial decision support system for guiding focal indoor residual
spraying interventions in a malaria elimination zone
4.1 Context
Chapter 3 explores the role of modern Geographical Reconnaissance (GR) to define the
spatial distribution of target populations in malaria elimination zones and provide a high-
resolution baseline dataset for the establishment of a spatial decision support system (SDSS)
framework to support elimination interventions. A key operational priority of malaria
elimination is the need to ensure that essential services and interventions are delivered at
optimal levels of coverage in target areas. Robust health systems are required to effectively
target, implement and deliver these scaled-up interventions. In many malaria endemic
countries, where health systems are already heavily constrained by meeting the delivery of routine healthcare, malaria elimination presents significant operational challenges. As outlined in the Roll Back Malaria (RBM) Global Malaria Action Plan (GMAP), there is a need for continued research into new tools and approaches to support intensified malaria
control and elimination campaigns.
In this chapter, a SDSS framework was used to support the implementation of focal indoor
residual spraying (IRS) interventions on Tanna Island in Tafea Province, Vanuatu. Like many
malaria endemic settings, Tafea Province faces operational and logistical challenges
including remote and dispersed communities with difficult access, little transport and
communication infrastructure, and limited human resources.
52
This study focuses on the adoption of a SDSS to support the effective implementation and delivery of focal IRS on Tanna at an optimal level of coverage. Specific aims were to develop a SDSS to: identify the focal IRS target area and delineate operation zones to support resource allocation and deployment; monitor and assess spray coverage by population and household throughout the implementation of IRS; map IRS service delivery at a household level; and to evaluate the user acceptability of the SDSS.
Findings from this chapter are significant in relation to the current call for research into new tools and approaches to support malaria elimination and the need for malaria programmes pursuing elimination to ensure the delivery of essential services and interventions at optimal levels of coverage in target areas. This study demonstrates the role of a SDSS to effectively coordinate, monitor and assess the implementation of a priority frontline intervention for malaria elimination and ensure that optimal levels of coverage are achieved within designated target areas.
4.2 Details of Publication
This chapter was published in the peer-reviewed journal Geospatial Health in 2011.
Kelly GC, Moh Seng C, Donald W, Taleo G, Nausien J, Batarii W, Iata H, Tanner M,
Vestergaard LS, Clements AC: A spatial decision support system for guiding focal
indoor residual spraying interventions in a malaria elimination zone. Geospat
Health 2011, 6:21-31.
53 Abstract
A customized geographical information system (GIS) has been developed to support focal indoor residual spraying (IRS) operations as part of a scaled-up campaign to
progressively eliminate malaria in Vanuatu. The aims of the GIS- based spatial
decision support system (SDSS) were to guide the planning, implementation and
assessment of IRS at the household level. Additional aims of this study were to
evaluate the user acceptability of a SDSS guiding IRS interventions. IRS was
conducted on Tanna Island, Republic of Vanuatu between 26 October and 5
December 2009. Geo-referenced household information provided a baseline within
the SDSS. An interactive mapping interface was used to delineate operation areas,
extract relevant data to support IRS field teams. In addition, it was used as a
monitoring tool to assess overall intervention coverage. Surveys and group
discussions were conducted during the operations to ascertain user acceptability.
Twenty-one operation areas, comprising a total of 187 settlements and 3,422
households were identified and mapped. A total of 3,230 households and 12,156
household structures were sprayed, covering a population of 13,512 individuals,
achieving coverage of 94.4% of the households and 95.7% of the population. Village
status maps were produced to visualize the distribution of IRS at the sub-village level.
One-hundred percent of survey respondents declared the SDSS a useful and effective
tool to support IRS. The GIS-based SDSS adopted in Tanna empowered programme
managers at the provincial level to implement and asses the IRS intervention with the
degree of detail required for malaria elimination. Since completion, SDSS
applications have expanded to additional provinces in Vanuatu and the neighbouring
Solomon Islands supporting not only specific malaria elimination and control
interventions, but also the broader public health sector in general.
54
Keywords
Geographical information systems, malaria elimination, indoor residual spraying,
spatial decision support system, Republic of Vanuatu
URL: http://www.geospatialhealth.unina.it/articles/v6i1/gh-v6i1-05-kelly.pdf
4.3 Supplementary Information
Supplementary data on the household distribution of long-lasting insecticidal nets
(LLIN)generated from the malaria elimination SDSS used in Temotu Province, Solomon
Islands, from 2008-2010 has been included as Appendices D and E.
55 Geospatial Health 6(1), 2011, pp. 21-31 A spatial decision support system for guiding focal indoor residual spraying interventions in a malaria elimination zone
Gerard C. Kelly1, Chang Moh Seng2, Wesley Donald3, George Taleo3, Johnny Nausien3, Willie Batarii4, Harry Iata3, Marcel Tanner5,6, Lasse S. Vestergaard2, Archie C. A. Clements1
1University of Queensland, School of Population Health, Brisbane, Australia; 2World Health Organization, Country Liaison Office, Port Vila, Vanuatu; 3Ministry of Health, Vector Borne Disease Control Programme, Port Vila, Vanuatu; 4Ministry of Health and Medical Services, Vector Borne Disease Control Programme, Honiara, Solomon Islands; 5Swiss Tropical and Public Health Institute, Basel, Switzerland; 6University of Basel, Basel, Switzerland
Abstract. A customized geographical information system (GIS) has been developed to support focal indoor residual spray- ing (IRS) operations as part of a scaled-up campaign to progressively eliminate malaria in Vanuatu. The aims of the GIS- based spatial decision support system (SDSS) were to guide the planning, implementation and assessment of IRS at the household level. Additional aims of this study were to evaluate the user acceptability of a SDSS guiding IRS interventions. IRS was conducted on Tanna Island, Republic of Vanuatu between 26 October and 5 December 2009. Geo-referenced household information provided a baseline within the SDSS. An interactive mapping interface was used to delineate oper- ation areas, extract relevant data to support IRS field teams. In addition, it was used as a monitoring tool to assess over- all intervention coverage. Surveys and group discussions were conducted during the operations to ascertain user accept- ability. Twenty-one operation areas, comprising a total of 187 settlements and 3,422 households were identified and mapped. A total of 3,230 households and 12,156 household structures were sprayed, covering a population of 13,512 indi- viduals, achieving coverage of 94.4% of the households and 95.7% of the population. Village status maps were produced to visualize the distribution of IRS at the sub-village level. One hundred percent of survey respondents declared the SDSS a useful and effective tool to support IRS. The GIS-based SDSS adopted in Tanna empowered programme managers at the provincial level to implement and asses the IRS intervention with the degree of detail required for malaria elimination. Since completion, SDSS applications have expanded to additional provinces in Vanuatu and the neighbouring Solomon Islands supporting not only specific malaria elimination and control interventions, but also the broader public health sec- tor in general.
Keywords: geographical information system, malaria elimination, indoor residual spraying, spatial decision support system, Republic of Vanuatu.
Introduction ic margins inward (i.e. shrinking the malaria map); and (iii) continued research into new tools, Of the current 99 countries with endemic malaria, approaches and interventions for malaria control and 32 have now committed to some kind of elimination elimination. strategy (Feachem et al., 2010). Key strategies to sup- The Republic of Vanuatu is the southern and east- port the eventual eradication of malaria have been ern-most malaria-endemic country in the South outlined in the Roll Back Malaria (RBM) Global Pacific (Feachem et al., 2010). As part of the GMAP Malaria Action Plan (GMAP) and focus on three strategy to shrink the malaria map from the endem- major approaches (GMAP, 2008; Feachem et al., ic margins inward, the Government of Vanuatu, 2009, 2010). These include: (i) aggressive and sus- with support from the Australian Agency for tained malaria control in highly endemic countries; International Development (AusAID) and the Pacific (ii) progressive malaria elimination from the endem- Malaria Initiative (PacMI) programme, is currently implementing a progressive malaria elimination campaign starting in the Tafea province in southern Corresponding author: Vanuatu. Priority interventions by programme type Gerard C. Kelly have been recommended by the World Health University of Queensland, School of Population Health Brisbane, Australia Organization (WHO) to guide elimination in coun- Tel. +61 0 423 443 932; Fax +61 0 266 531 245 tries of low and moderate endemicity (WHO, 2007). E-mail: [email protected] Following these guidelines, Vanuatu has committed 22 G.C. Kelly et al. - Geospatial Health 6(1), 2011, pp. 21-31 to total indoor residual spray (IRS) coverage in focal operations in Tanna (Kelly et al., 2010), the Vanuatu “hotspot” areas as one of the primary front-line National Vector Borne Disease Control Programme interventions during the pre-elimination programme (VBDCP) is committed to exploring an SDSS phase. approach as a mechanism to support the scaled-up A major obstacle to the scale-up of services in malar- demands of malaria elimination. This study focuses ia-endemic countries is weak health information sys- on the use of a customised, GIS-based SDSS to sup- tems and surveillance needed to monitor the progress port first-round focal IRS in Tanna. The aims of the of effective public health responses and/or programme study were to develop an applied map-based tool to adjustments (Vitoria et al., 2009; Kerouedan, 2010). assist in the delineation of IRS zones to support Additionally, the delivery of health services and key resource allocation and deployment; to monitor and interventions in resource-poor environments at cover- assess spray coverage by population and household age levels the target population should be able to ben- throughout the intervention; and to map the respec- efit from, is still a major challenge (WHO, 2009; tive spatial distribution of IRS service delivery at the UNICEF/UNDP/World Bank/WHO, 2010; The household level. This study also provided an oppor- malERA Consultative Group on Health Systems tunity to evaluate the user acceptability of a SDSS in Operational Research, 2011). During the pre-elimina- meeting the monitoring and evaluation responsibili- tion stage, malaria programmes must have the capac- ties of a malaria elimination programme. ity to implement surveillance, reporting and informa- tion systems (WHO, 2007; GMAP, 2008). Materials and methods Malaria elimination is distinct from control in its requirement for the geographical targeting of Study area resources for key interventions (Feachem et al., 2009). In the context of focal IRS for elimination in Tafea, Focal IRS was conducted on Tanna Island in key operational challenges include the efficient alloca- province of Tafea, Republic of Vanuatu, an area tion of essential resources such as insecticide, equip- selected for malaria elimination (Fig. 1). Following ment and manpower to remote locations. The devel- baseline entomological surveys to describe the dis- opment of an effective monitoring, surveillance and tribution of the vector Anopheles farauti (The reporting mechanism to ensure that maximum house- PacMI Survey Group on behalf of the Ministries of hold spray coverage is uniformly achieved across the Health of Vanuatu and Solomon Islands, 2010) and entire target area is essential. geo-statistical analysis of malaria survey data on the With the growing application of geographical infor- main island of Tanna in 2008 (Reid et al., 2010), mation systems (GIS), global positioning systems focal IRS operational areas on Tanna was defined as (GPS) and web-based mapping technology, spatial all settlements located within a 2 km boundary of analysis in disease management and health planning is the coastline. As part of the Tafea province elimina- now well established (e.g. Lozano-Fuentes et al., 2008; tion strategy, three annual rounds of IRS were Clements et al., 2009; Daash et al., 2009; Srivastava et planned for the Tanna focal IRS zone between 2009 al., 2009; Reid et al., 2010). In the context of malaria and 2011. The first round of IRS, consisting of a elimination, the need for modernized, high-resolution main spraying campaign immediately followed up to mapping to support the operational management of cover households not reached by the initial round, scaled-up interventions is also recognized (The malEra was conducted between 26 October and 5 Consultative Group on Monitoring Evaluation and December, 2009. Surveillance, 2011). A spatial decision support system Approval to conduct IRS was provided by the (SDSS) is an integrated database management system Vanuatu Ministry of Health (MoH). Ethical (usually GIS-based) that provides computerised sup- approval was not sought during this study as IRS is port for decision making where there is a geographic considered a routine operational activity of the or spatial component available (Keenen, 2003). national malaria programme, with all collected data Currently, only limited research and action has been managed as per confidentiality requirements of the undertaken on applied applications of SDSS to guide Vanuatu MoH. All IRS fieldwork was conducted by malaria elimination (and as a mechanism strengthen VBDCP staff and casual employees contracted by health information systems in general). the MoH with training and technical assistance pro- Following the completion of geographical recon- vided by WHO and the PacMI Support Centre naissance (GR), household mapping and enumeration (PacMISC). G.C. Kelly et al. - Geospatial Health 6(1), 2011, pp. 21-31 23
Fig. 1. Location of Tanna Island, Vanuatu.
Spatial decision support system development agement framework. These included detailed popula- tion and building structure data collected by house- MapInfo Professional and MapBasic (Pitney Bowes hold. Hydrographic, altitude, road and other infra- Software Inc., Troy, NY, USA) were used as the GIS structure data were also acquired from relevant part- software platform of the SDSS and for the develop- ner ministries to provide baseline topographic infor- ment of customized SDSS application within the GIS, mation. Specific applications developed within the respectively. Microsoft Excel and Microsoft Access SDSS were designed to provide interactive and auto- (Microsoft Corporation, Redmond, WA, USA) soft- mated support for key components of IRS manage- ware were used for additional integrated data man- ment including planning, implementation, monitoring, agement and analysis. Topographic and household and reporting. Table 1 provides a breakdown of the data previously acquired during GR (Kelly et al., specific technical applications developed within the 2010) provided the basis for the geospatial IRS man- SDSS.
Table 1. Technical components of the indoor residual spraying (IRS) customised spatial decision support system (SDSS).
IRS management SDSS technical function Application for the IRS intervention component GIS buffering to define focal IRS zone Develop a map of the 2 km coastal IRS zone Interactive mapping to define operation areas Used to breakdown and map the IRS zone into smaller manageable areas Planning Automated query to extract household, spray-able structure Application to provide summary details of the operation and population summary data by operation area area to support the planning of required resources and timeframes Automatic generation of IRS hardcopy checklists by Application to provide detailed checklists for household IRS field teams Implementation Interactive operation area household location mapping Development of operation area maps to aid application IRS field teams Automatic IRS status thematic mapping from hardcopy Used to monitor the progress of IRS in the field at the checklist data household level via a map interface Automatic generation of IRS follow-up lists by household Application to extract a detailed list of households not Monitoring sprayed during the initial round Interactive IRS household follow-up mapping application Development of follow-up maps highlighting households not sprayed Automated IRS status summary reports via Microsoft Development of reports to measure IRS coverage Reporting Access interface Automated IRS status thematic map generation Development of IRS coverage spatial distribution maps 24 G.C. Kelly et al. - Geospatial Health 6(1), 2011, pp. 21-31
Spatial decision support system operations training tion including: (i) a unique household identification number; (ii) name of the household head; (iii) the Development of the customized SDSS took place at household population; (iv) the village name; and (v) a the provincial level in consultation with Vanuatu detailed breakdown of the number and type of spray- VBDCP malaria information officers and the Tafea able structures per household. Additional fields were province malaria supervisor. As part of an introduc- added to the hardcopy checklist to record IRS progress tion into the customized SDSS system for IRS man- including: (i) number and type of structures sprayed agement, standard operating procedures (SOPs) were per household; (ii) IRS household spray status; and developed and technical training provided to the (iii) additional comments field to record reasons for national and Tafea VBDCP malaria information offi- households remaining “not sprayed” or “partially cers over a 3-day period. Following the initial intro- sprayed”. Household location maps were produced duction and training period, the SDSS was operated for each IRS operation area to provide a simple navi- independently by the national and provincial VBDCP gational tool to assist spray teams to locate house- information officers throughout the first round of IRS holds whilst in the field. IRS teams were issued hard- interventions. copy household checklists and location maps, and briefed on their correct usage prior to commencing Implementation of IRS field operations.
The pre-defined focal IRS zone was mapped in the Monitoring the progress of IRS GIS to produce a polygon layer of all inland areas within a 2-km boundary of the coastline. All geo-ref- All household checklist information was updated erenced household information collected during GR into a centralized spreadsheet prior to the commence- and household mapping operations was then extract- ment of IRS. As these were completed, hardcopy ed to provide a total summary of the IRS target area checklists were sent to the provincial headquarters for by household and population. Prior to the deployment data entry. All data entry was carried out by the of IRS field teams, surface areas of all indoor surfaces provincial information officers concurrently with IRS deemed spray-able were estimated from a selection of field operations. As the IRS data were updated, the 40 households (10 per individual health zone on household spray status was thematically mapped Tanna) to allow calculating insecticide volume through an automated application. Following the requirements. completion of the initial first-round IRS, hard-copy The 2-km coastal IRS zone was broken down into lists of all households not sprayed were automatically individual operational areas to support the planning generated and re-issued to spray teams to conduct the and allocation of required resources and deployment follow-up campaign. Maps of households not sprayed of field teams. Using the customized SDSS application, were produced to provide a navigational aid for the area boundaries were defined and digitally mapped by field teams. Upon completion, follow-up household VBDCP personnel based on the spatial distribution of spray data were entered into the spreadsheet as per the households, terrain, logistical constraints and existing first-round operations and automatically updated into local knowledge of the IRS focal zone. Household, the SDSS. spray-able structures and population summary data were automatically generated as area boundaries were Assessment of SDSS management framework defined to assist the estimation of resources needed. Eighteen IRS field teams consisting of four individu- Basic descriptive statistics of selected indicators als per team were used to carry-out IRS. These teams were used to analyse the data generated by the SDSS were managed by one overall IRS coordinator and and to assess IRS coverage at the completion of both three field supervisors. One provincial information first-round and follow-up IRS. These data included: officer was responsible for the monitoring and evalua- (i) total number of settlements visited; (ii) total num- tion of the IRS and supported by the national-based ber of households visited; (iii) total population; (iv) VBDCP information officer as required. Household number of households sprayed; (v) total population spray lists were extracted using the SDSS and export- covered; (vi) number of household refusals; (vii) per- ed into hardcopy templates, providing household centage of household spray coverage; and (viii) per- checklists for IRS field teams. Hardcopy checklists centage of population coverage. Automated SDSS provided the teams with detailed household informa- data reports were generated via a Microsoft Access G.C. Kelly et al. - Geospatial Health 6(1), 2011, pp. 21-31 25 interface to provide routine feedback regarding tion of IRS coverage on Tanna at the completion of progress during the intervention and an overall sum- both the initial main round and follow-up IRS stages. mary of the first-round IRS. Coverage maps were also Table 3 provides a breakdown of IRS coverage data by produced. health zone following the completion of first-round Operational SDSS errors were monitored through- interventions. Table 4 also provides a breakdown of out the course of the intervention in Tanna. SDSS user household structures by type sprayed during first- acceptability surveys based on a five-point Likert scale (1932) range and open response were also administered to VBDCP information officers, IRS coordinators and supervisors. To increase the number of SDSS operators providing data for analysis, we also administered surveys to the relevant VBDCP staff in the Solomon Islands, where a similar SDSS-based approach to IRS had been adopted. A total of 12 SDSS user acceptability surveys were administered in Vanuatu and the Solomon Islands. Post-fieldwork debrief sessions were also conducted with all IRS field teams to interview participants about their individual experiences and perceptions of the SDSS framework and associated applications. Additionally, partici- pants were also asked to give insight into potential improvements and additional applications relevant to the malaria elimination programme in Tafea.
Results
Twenty-one IRS operation areas, comprising a total of 187 settlements and 3,422 households, were Fig. 2. Tanna Island IRS operation areas map. defined and mapped by the provincial VBDCP malar- ia information officer using the interactive SDSS map- Table 2. Operation area summary data generated by the cus- ping interface (Fig. 2). Table 2 shows the target num- tomised SDSS. ber of households, sprayable structures and popula- Operation Total Total Sprayable Total tion in each IRS operation area, generated by the SDSS zone settlements households structures population and used to guide IRS planning and resource alloca- 1 3 52 193 227 tion. Fig. 3 provides a screenshot of the IRS planning 2 1 53 206 216 SDSS interface used to generate the IRS household 3 2 93 328 361 spray lists and operation area summaries. 4 4 73 228 339 5 3 49 136 210 6 7 52 179 205 Summary of first-round IRS interventions 7 7 106 380 543 8 9 123 457 523 A total of 3,015 households and 11,737 household 9 7 95 339 431 structures were sprayed following the initial round of 10 6 83 295 367 11 8 108 365 420 IRS, covering a population of 12,762 individuals. The 12 5 158 534 609 initial first-round spray coverage was 88.1% of house- 13 6 198 611 779 holds and 91.3% of household structures, covering 14 7 124 434 427 90.4% of the population. Following the completion of 15 3 104 418 388 follow-up IRS, a total of 3,230 households and 12,156 16 10 207 754 825 17 14 267 1054 1,075 household structures were sprayed, covering a popula- 18 11 194 662 802 tion of 13,512 individuals. The final, first-round IRS 19 23 228 1,041 1,048 spray coverage was recorded as 94.4% of households 20 25 514 2,226 2,294 and 94.6% household structures, covering 95.7% of 21 26 541 2,013 2,030 the population. Fig. 4 illustrates the spatial distribu- Total 187 3,422 12,853 14,119 26 G.C. Kelly et al. - Geospatial Health 6(1), 2011, pp. 21-31
Fig. 3. Screenshot of the IRS planning SDSS application.
Fig. 4. Spatial distribution of IRS coverage prior to and following the first-round follow-up operations. G.C. Kelly et al. - Geospatial Health 6(1), 2011, pp. 21-31 27
Table 3. Tanna Island first-round IRS coverage, stratified by health zone
Total recorded Sprayed (initial round) Sprayed (follow-up) Total sprayed Health Settlements zone visited Households Population Households Population Households Population Households Population 87.1% 90.5% 6.2% 5.3% 93.3% 95.8% TAF 01 74 1,283 5,372 (1,118) (4,861) (79) (284) (1,197) (5,145) 95.2% 95.4% 3.2% 2.6% 98.4% 98.1% TAF 02a 20 372 1,558 (354) (1,487) (12) (41) (366) (1,528)
TAF 03a 38 772 3,090 89.9% 91.5% 6.5% 5.5% 96.4% 97.00% (694) (2,826) (50) (171) (744) (2,997)
TAF 04a 55 995 4,099 85.3% 87.5% 7.4% 6.2% 92.8% 93.7% (849) (3,588) (74) (254) (923) (3,842)
Total 187 3,422 14,119 88.1% 90.4% 6.3% 5.3% 94.4% 95.7% (3,015) 12,762) (215) (750) (3,230) (13,512) round IRS activities. Fig. 5 provides a screenshot of surface area and the number of total households the SDSS monitoring application used to generate sprayed, the insecticide dosage rates were estimated at household follow-up spray lists has been provided as 28 mg active ingredient/m2, falling within the WHO an additional file. pesticide evaluation scheme (WHOPES) recommended Only 192 of the 3,422 recorded households were dosage of 20-30 mg active ingredient/m2 (WHO, not sprayed at the completion of the follow-up IRS 2009). round on Tanna. Table 5 shows the main reasons for houses not sprayed. Seventy-five refusals and 73 Assessment of the SDSS management framework locked households were recorded, attributing to 39.1% and 38.0% of total households not sprayed, During the planning and implementation of the IRS respectively. Other reasons households were not rounds on Tanna, no operational errors relating to use sprayed included households serving as food store or of the SDSS system by the IRS teams were identified or canteen, general inaccessibility and because of sick res- reported. The acceptability among the users of the idents. Fig. 6 shows an example of an IRS village sta- SDSS was excellent, with 12/12 (100%) respondents tus map visualizing the spatial distribution of house- of the acceptability questionnaire stating that the SDSS holds not sprayed to identify any clustering at the sub- is a useful and effective tool for planning, monitoring village level, both during and after completion of the and reporting of IRS (Table 6). Common themes high- initial and the follow-up rounds of IRS. lighted from additional comments and mentioned dur- Prior to the commencement of IRS, the average ing debriefing with IRS personnel were centred on household surface area was estimated at 168 m2. The interest in employing the SDSS application in the first-round IRS operations used 2,760 ICON® 10CS malaria control provinces where IRS is currently insecticide sachets (62.5 ml per sachet containing 10% implemented or planned, and the need to continue active ingredient) indicating total insecticide consump- developing the SDSS to support additional priority tion at 172.5 litres. Based on the average household interventions specific to malaria elimination.
Table 4. IRS coverage breakdown by household structure.
Sprayed in Sprayed (%) Sprayed during Sprayed (%) Structure type Total recorded Total sprayed first-round (first-round) follow-up (final) Sleeping house 4,895 4,489 91.7 171 4,660 95.2 Kitchen 2,689 2,492 92.7 74 2,566 95.4 Garden house 15 14 93.3 0 14 93.3 Rest shelter 748 687 91.8 14 701 93.7 Nakamal 255 219 85.9 10 229 89.8 Toilet 2,182 2,022 92.7 51 2,073 95.0 Other structures 2,069 1,814 87.7 99 1,913 92.5 Total 12,853 11,737 91.3 419 12,156 94.6 28 G.C. Kelly et al. - Geospatial Health 6(1), 2011, pp. 21-31
Fig. 5. Screenshot of the IRS household follow-up SDSS application.
Table 5. Reasons households were not sprayed at the comple- tion of IRS.
Reason Total households Refusal 75
Locked house 73
Inaccessible 5
Sick resident 4
Food store/canteen 20
Other 15
Total 192 Fig. 6. Example Tanna Island IRS household status map at a sub-village scale.
Table 6. SDSS user acceptability survey response summary data.
Strongly Strongly Survey Question Agree Undecided Disagree agree disagree 100% 0% 0% 0% 0% The SDSS provides a useful tool for planning IRS activities (12) 0 0 0 0 The SDSS provides a useful tool for monitoring IRS and 100% 0% 0% 0% 0% supporting follow-up operations (12) 0 0 0 0 The SDSS is useful for generating IRS status reports and 100% 0% 0% 0% 0% maps at the completion of operations (12) 0 0 0 0 The SDSS and mapping applications were easy to 75% 17% 8% 0% 0% understand and use (9) (2) (1) 0 0 Household mud-maps are a useful tool to support IRS 100% 0% 0% 0% 0% in the field (12) 0 0 0 0 75% 25% 0% 0% 0% Household mud-maps are easy to interpret in the field (9) (3) 0 0 0 The SDSS is a more effective tool to coordinate IRS than 92% 8% 0% 0% 0% traditional planning and reporting mechanisms (11) (1) 0 0 0 G.C. Kelly et al. - Geospatial Health 6(1), 2011, pp. 21-31 29
Discussion is essential to ensure that service delivery within the target area is uniformly distributed to minimise the This paper presents an innovative approach current- potential for sustained transmission. Traditionally, ly adopted in Vanuatu that utilises advancements in malaria endemic countries with weak health informa- digital geo-spatial mapping technologies to coordinate tion systems have had limited capacity to promptly and monitor an IRS intervention within a malaria and effectively measure the progress and spatial distri- elimination zone. Development of the SDSS for IRS bution of interventions. Through the automated map- focused on building from and utilising the existing ping and reporting of IRS status by household within geospatial household data collected during previous the SDSS, program managers were able to interactive- GR surveys. Key facets of the IRS intervention indi- ly monitor the progress and visualise the spatial distri- vidually considered in the SDSS design included plan- bution of coverage during implementation, without ning, implementation, monitoring and reporting. the need for complex statistical analysis. The ability to Programme management priorities during focal IRS view IRS status at a detailed level and automatically operations reflected the challenges of implementing a extract associated household data for immediate fol- scaled-up elimination intervention in the context of a low-up response provides an effective operational tool remote Pacific island, where access to resources and to ensure coverage is both maximised and evenly dis- infrastructure are limited. As such, priorities focused tributed. on the need for efficient resource allocation, effective Data generated by the SDSS at the completion of the service delivery (i.e. maximizing household spray cov- first-round IRS pertaining to households not sprayed erage), and a high resolution yet user-friendly (Table 5) provides useful information supporting the approach to monitoring, evaluation, surveillance and malaria elimination programme. These data, coupled reporting. with the IRS coverage distribution maps produced at a During the planning phase, the interactive mapping sub-village scale (as illustrated in Fig. 6), can enable interface enabled VBDCP personnel to visualize the programme managers to identify trends in service distribution of households within the target area in delivery such as clusters of locked households or relation to terrain and other logistical factors such as refusals. This provides a mechanism to assess indica- access and transport infrastructure. Based on these tors such as the effectives of IRS notification cam- characteristics, the IRS target zone was broken down paigns and the operational performance of individual into the 21 manageable operation areas. Data generat- spray teams. Additionally, these data can be used to ed for each area (Table 2) provided a basis for the strategically plan and target further response measures development of operational timeframes, allocation of such as community-based awareness and behaviour transport, and the geographical targeting of key change communication (BCC) interventions in appro- resources, including insecticide, spray equipment and priate geographical regions. personnel to designated locales. In remote regions, As direct interaction with the SDSS interface is gen- where access is limited, the ability to accurately esti- erally only relevant to information officers, programme mate resource requirements prior to deployment is managers and supervisors; the quantity of user accept- essential to avoid logistical shortcomings such as the ability survey data collected was limited. However, raw inadequate supply of equipment and manpower. data collected from these surveys in both Vanuatu and As IRS is also a method of community protection, the Solomon Islands still demonstrates a high user maximum impact on malaria transmission is achieved acceptability of the SDSS, with 100% of respondents by reaching the highest level of coverage possible highlighting its usefulness as an operational tool for the (WHO, 2006). Data presented in the results indicate planning, implementation, monitoring and evaluation that IRS coverage across the target area in Tanna was of IRS interventions. While we acknowledge that the high, suggesting an overall effective implementation of small sample size prohibits meaningful statistical analy- the intervention. Similarly, insecticide dosage rates cal- sis and limits the ability to draw conclusions, the culated by average household surface area estimates results of the acceptability study are encouraging. The and total insecticide used also provides a preliminary high acceptability of the SDSS is reflected in the re- indication of an overall effective spray application adoption and expansion of the SDSS as the primary during the first-round IRS. operational tool to guide subsequent IRS interventions Spatial coverage data illustrated no significant het- in Tafea province, other malaria control provinces in erogeneity that could have led to remaining pockets of Vanuatu, and focal IRS operations in the neighbouring transmission. In the context of malaria elimination, it Solomon Islands. Both countries have now also 30 G.C. Kelly et al. - Geospatial Health 6(1), 2011, pp. 21-31 expanded the SDSS framework to support additional future integration and geo-referencing of additional frontline elimination interventions including universal programme data, including entomology, mobility and household distribution of long-lasting insecticidal nets malaria case data. Through the incorporation of IRS (LLINs). household data with GR, additional intervention and Constraints of this field study are largely related to other programme data, the SDSS framework provides paper-based data entry and included minor lag time an effective building block for the continuous develop- from field operations to digital data entry, and data ment of a geographical-based malaria elimination entry formatting errors. To mitigate these constraints, household intervention database. It is anticipated the hardcopy checklists were collected from the IRS field SDSS principles adopted in Vanuatu will be expanded teams regularly and digital data entry field formats to not only continue supporting future malaria elimi- were locked. However, it is commonly indicated in the nation priorities in the Pacific such as targeted geospa- literature that hand-held data entry technology such as tial surveillance, rapid response and case investigation, Personal Digital Assistant (PDAs) and mobile phones but also be used as an interactive geospatial decision for field-based survey is cost-effective (Forster et al., support framework for the broader health sector in 1991; Fletcher et al., 2003; Shirima et al., 2007; Yu et general. al., 2009). Should resources such as digital hand-held units be available, the potential to integrate such tech- Acknowledgements nology into an SDSS operational management frame- work would further increase reporting efficiency and We would like to thank all of the individuals from the provide programme managers with detailed indicative Vanuatu VBDCP and community volunteers who assisted with data in real-time. Additionally, it is anticipated that the IRS fieldwork on Tanna Island, particularly the leadership of the expanding array of open-source and web-based James Iaviong and Rueban Sumaki. We would also like to thank geospatial applications becoming available today will the PacMISC provincial programme support officer Megan ensure that the accessibility of geospatial decision Johnson for her administrative support throughout the IRS making support tools for malaria elimination, public campaign. Additionally, we would like to thank the survey par- heath, and health planning in general will continue to ticipants from the Solomon Islands VBDCP. Finally, we thank grow. the chiefs and communities of Tanna Island for their continued A major focus in the development of the SDSS was participation, cooperation and overwhelming support in these the provision of provincially based public health operations and the malaria elimination programme in general. workers with a user-friendly, yet powerful GIS-based A.C.A. Clements is supported by a Career Development Award operational tool that could support all phases of pro- from the Australian National Health and Medical Research gramme implementation and be operated independ- Council (#631619). ently by the field-based decision makers themselves. When used as part of a scaled-up frontline malaria References elimination strategy, targeted vector-control activities such as IRS require sensitive tools to spatially monitor Clements AC, Barnett AG, Cheng ZW, Snow RW, Zhou HN, and evaluate intervention coverage to ensure maxi- 2009. Space-time variation of malaria incidence in Yunnan mum efficacy and universal service delivery is province, China. Malar J 8, 180. achieved. The employment of a SDSS during the first Daash A, Srivastava A, Nagpal BN, Saxena R, Gupta SK, 2009. round of IRS in Tanna provided the Tafea VBDCP Geographical information system (GIS) in decision support to programme with a tool to guide all key facets of the control malaria—a case study of Koraput district in Orissa, intervention at the level of detail required for malaria India. J Vector Borne Dis 46, 72-74. elimination. The geo-spatial framework provided a Feachem RGA, Phillips AA, Hwang J, Cotter C, Wielgosz B, user-friendly approach to visualize and breakdown the Greenwood BM, Sabot O, Rodriguez MH, Abeyasinghe RR, IRS target zone into manageable operation areas and Ghebreyesus TA, Snow RW, 2010. Shrinking the malaria map: extract detailed information to support the planning, progress and prospects Lancet 376, 1566-1578. implementation, monitoring and evaluation of the Feachem RGA, Phillips AA, Targett GA (Eds), 2009. Shrinking intervention. Additionally, the SDSS provided a visual the malaria map: a prospectus on malaria elimination. San and effective mechanism to assess service delivery, Francisco, The Global Health Group, Global Health Sciences, identify potential gaps and strategically target respon- University of California, 187 pp. sive measures to relevant geographic locations. This Fletcher LA, Erickson DJ, Toomey TL, Wagenaar AC, 2003. framework now also provides a foundation for the Handheld computers. A feasible alternative to paper forms for G.C. Kelly et al. - Geospatial Health 6(1), 2011, pp. 21-31 31
field data collection. Eval Rev 27, 165-178. tribal state of India: a GIS based approach. Int J Health Geogr Forster D, Behrens RH, Campbell H, Byass P, 1991. Evaluation 8, 30. of a computerized field data collection system for health sur- The malERA Consultative Group on Health Systems veys. Bull World Health Organ 69, 107-111. Operational Research, 2011. A research agenda for malaria GMAP, 2008. The Global Malaria Action Plan, Roll Back eradication: health systems and operational research. PLoS Malaria Partnership, 271 pp. Med 8, e1000397. Keenen PB, 2003. Spatial decision support systems. Decision The malEra Consultative Group on Monitoring Evaluation and making support systems achievements and challenges for the Surveillance, 2011. A research agenda for malaria eradication: new decade. Morah M, Forgionne G, Gupta J, Hershey PA monitoring, evaluation, and surveillance. PLoS Med 8, (Eds), Idea Group Publishing, pp. 28-39. e1000400. Kelly GC, Hii J, Batarii W, Donald W, Hale E, Nausien J, The Pacific Malaria Initiative Survey Group on behalf of the Pontifex S, Vallely A, Tanner M, Clements A, 2010. Modern Ministries of Health of Vanuatu and Solomon Islands, 2010. geographical reconnaissance of target populations in malaria Malaria on isolated Melanesian islands prior to the initiation elimination zones. Malar J 9, 289. of malaria elimination activities. Malar J 9, 218. Kerouedan D, 2010. The Global Fund to fight HIV/AIDS, TB UNICEF/UNDP/World Bank/WHO, 2010. Special Programme and Malaria 5-y: evaluation policy issues. Bull Soc Pathol Exot for Research & Training in Tropical Diseases (TDR). 103, 119-122. Retrieved 08/09/2010, 2010, from http://apps.who.int/ Likert R, 1932. A technique for the measurement of attitudes. tdr/svc/topics/health-systems-implementation-research. Arch Psych 140, 1-55. Vitoria M, Granich R, Gilks CF, Gunneberg C, Hosseini M, Lozano-Fuentes S, Elizondo-Quiroga D, Farfan-Ale JA, Lorono- Were W, Raviglione M, De Cock KM, 2009. The global fight Pino MA, Garcia-Rejon J, Gomez-Carro S, Lira-Zumbardo V, against HIV/AIDS, tuberculosis, and malaria: current status Najera-Vazquez R, Fernandez-Salas I, Calderon-Martinez J, and future perspectives. Am J Clin Pathol 131, 844-848. Dominguez-Galera M, Mis-Avila P, Morris N, Coleman M, WHO, 2006. Indoor residual spraying. Use of indoor residual Moore CG, Beaty BJ, Eisen L, 2008. Use of Google Earth to spraying for scaling up global malaria control and elimination. strengthen public health capacity and facilitate management of World Health Organization, Geneva, Switzerland, pp. 10. vector-borne diseases in resource-poor environments. Bull WHO, 2007. Malaria elimination - a field manual for low and World Health Organ 86, 718-725. moderate endemic countries. World Health Organization, Reid H, Haque U, Clements AC, Tatem AJ, Vallely A, Ahmed Geneva, Switzerland, pp. 85. SM, Islam A, Haque R, 2010. Mapping malaria risk in WHO, 2009. WHO recommended insecticides for indoor resid- Bangladesh using Bayesian geostatistical models. Am J Trop ual spraying against malaria vectors. Retrieved at Med Hyg 83, 861-867. http://www.who.int/whopes/Insecticides_IRS_Malaria_09.pdf. Shirima K, Mukasa O, Schellenberg JA, Manzi F, John D, Mushi (accessed on January 2011). A, Mrisho M, Tanner M, Mshinda H, Schellenberg D, 2007. WHO, 2009. World Malaria Report 2009. World Health The use of personal digital assistants for data entry at the Organization, Geneva, Switzerland. point of collection in a large household survey in southern Yu P, de Courten M, Pan E, Galea G, Pryor J, 2009. The devel- Tanzania. Emerg Themes Epidemiol 4, 5. opment and evaluation of a PDA-based method for public Srivastava A, Nagpal BN, Joshi PL, Paliwal JC, Dash AP, 2009. health surveillance data collection in developing countries. Int Identification of malaria hot spots for focused intervention in J Med Inform 78, 532-542.
Chapter 5
A high-resolution geospatial surveillance-response system for malaria
elimination in Solomon Islands and Vanuatu
5.1 Context
In Chapter 4, the role of a spatial decision support system (SDSS) to support the effective
implementation of a priority frontline intervention in a malaria elimination zone was
presented. As countries transition from the pre-elimination programme phase to elimination and the eventual prevention-of-reintroduction phase, an additional operational priority is the ability to conducted detailed surveillance to actively detect malaria infections, identify
transmission foci and target appropriate responses swiftly. In this chapter, a SDSS-based high-resolution geospatial surveillance-response framework developed to support progressive
malaria elimination in Solomon Islands and Vanuatu is presented.
For malaria elimination the goal is to halt localized transmission. Malaria programmes must
therefore have the capacity to swiftly detect and treat both local and imported malaria
infections through a combination of passive and active case detection, accurately classify and
locate transmission foci, and target appropriate response interventions before the occurrence
of secondary cases and perpetuation or reintroduction of local transmission. Whilst the
importance of surveillance for elimination is well recognised, malaria programmes and their
associated health systems often face significant operational challenges in effectively
implementing surveillance and response at the level of precision that is required.
67 In this chapter, a SDSS-based surveillance-response system was developed and implemented
in the malaria elimination provinces of Isabel and Temotu, Solomon Islands and Tafea,
Vanuatu, and monitored over a 12-month period. Specific aims of the study were to develop a
geospatial surveillance tool to automatically locate and map the distribution of reported
confirmed malaria cases by household; and explore the effectiveness of a SDSS-based
framework for supporting the rapid classification of active transmission foci and strategically
guiding appropriate targeted response measures through the identification of geographic areas
of interest based on the spatial and temporal distribution of cases.
Findings from this chapter are significant in relation to the current call for research into new
tools and approaches to support elimination and the operational priority of malaria
programmes pursuing elimination to swiftly detect, classify and attack infection foci. The
study conducted in this chapter demonstrates the application of a high-resolution SDSS-based surveillance-response system to support malaria elimination in remote Pacific Island settings.
5.2 Details of Publication
This chapter was published in the peer-reviewed journal Malaria Journal in 2013. Following publication, the manuscript was listed as a BioMed Central “Highly Accessed” journal article.
Kelly GC, Hale E, Donald W, Batarii W, Bugoro H, Nausien J, Smale J, Palmer K,
Bobogare A, Taleo G, Vallely A, Tanner M, Vestergaard LS, Clements AC: A high-
resolution geospatial surveillance-response system for malaria elimination in
Solomon Islands and Vanuatu. Malaria Journal 2013, 12:108.
68
Abstract
Background: A high-resolution surveillance-response system has been developed
within a geographic information system (GIS) to support malaria elimination in the
Pacific. This paper examines the application of a GIS-based spatial decision support
system (SDSS) to automatically locate and map the distribution of confirmed malaria
cases, rapidly classify active transmission foci, and guide targeted responses in
elimination zones.
Methods: Customized SDSS-based surveillance-response systems were developed in
the three elimination provinces of Isabel and Temotu, Solomon Islands and Tafea,
Vanuatu. Confirmed malaria cases were reported to provincial malaria offices upon
diagnosis and updated into the respective SDSS as part of routine operations
throughout 2011. Cases were automatically mapped by household within the SDSS
using existing geographical reconnaissance (GR) data. GIS queries were integrated
into the SDSS-framework to automatically classify and map transmission foci based
on the spatiotemporal distribution of cases, highlight current areas of interest (AOI)
regions to conduct foci-specific targeted response, and extract supporting household
and population data. GIS simulations were run to detect AOIs triggered throughout
2011 in each elimination province and conduct a sensitivity analysis to calculate the
proportion of positive cases, households and population highlighted in AOI regions of
a varying geographic radius.
Results: A total of 183 confirmed cases were reported and mapped using the SDSS
throughout 2011 and used to describe transmission within a target population of
69 90,354. Automatic AOI regions were also generated within each provincial SDSS
identifying geographic areas to conduct response. 82.5% of confirmed cases were
automatically geo-referenced and mapped at the household level, with 100% of
remaining cases geo-referenced at a village level. Data from the AOI analysis
indicated different stages of progress in each province, highlighting operational
implications with regards to strategies for implementing surveillance-response in
consideration of the spatiotemporal nature of cases as well as logistical and financial
constraints of the respective programmes.
Conclusions: Geospatial systems developed to guide Pacific Island malaria
elimination demonstrate the application of a high resolution SDSS-based approach to
support key elements of surveillance-response including understanding
epidemiological variation within target areas, implementing appropriate foci-specific
targeted response, and consideration of logistical constraints and costs.
Keywords
Malaria elimination; Surveillance; Geographic information systems (GIS); Spatial
decision support systems (SDSS); Mapping
URL: http://www.malariajournal.com/content/12/1/108
5.3 Supplementary Information
Additional materials included in the published manuscript are presented in the thesis as
Appendices F and G. A supplementary screenshot of the Isabel Province surveillance-
70 response SDSS illustrating the map-based and query-dialogue tools to support epidemiological investigation has also been provided as Appendix H.
71 Kelly et al. Malaria Journal 2013, 12:108 http://www.malariajournal.com/content/12/1/108
RESEARCH Open Access A high-resolution geospatial surveillance-response system for malaria elimination in Solomon Islands and Vanuatu Gerard C Kelly1*, Erick Hale2, Wesley Donald3, Willie Batarii2, Hugo Bugoro2, Johnny Nausien3, John Smale4, Kevin Palmer5, Albino Bobogare2, George Taleo3, Andrew Vallely6, Marcel Tanner1,7,8, Lasse S Vestergaard9 and Archie CA Clements1
Abstract Background: A high-resolution surveillance-response system has been developed within a geographic information system (GIS) to support malaria elimination in the Pacific. This paper examines the application of a GIS-based spatial decision support system (SDSS) to automatically locate and map the distribution of confirmed malaria cases, rapidly classify active transmission foci, and guide targeted responses in elimination zones. Methods: Customized SDSS-based surveillance-response systems were developed in the three elimination provinces of Isabel and Temotu, Solomon Islands and Tafea, Vanuatu. Confirmed malaria cases were reported to provincial malaria offices upon diagnosis and updated into the respective SDSS as part of routine operations throughout 2011. Cases were automatically mapped by household within the SDSS using existing geographical reconnaissance (GR) data. GIS queries were integrated into the SDSS-framework to automatically classify and map transmission foci based on the spatiotemporal distribution of cases, highlight current areas of interest (AOI) regions to conduct foci-specific targeted response, and extract supporting household and population data. GIS simulations were run to detect AOIs triggered throughout 2011 in each elimination province and conduct a sensitivity analysis to calculate the proportion of positive cases, households and population highlighted in AOI regions of a varying geographic radius. Results: A total of 183 confirmed cases were reported and mapped using the SDSS throughout 2011 and used to describe transmission within a target population of 90,354. Automatic AOI regions were also generated within each provincial SDSS identifying geographic areas to conduct response. 82.5% of confirmed cases were automatically geo-referenced and mapped at the household level, with 100% of remaining cases geo-referenced at a village level. Data from the AOI analysis indicated different stages of progress in each province, highlighting operational implications with regards to strategies for implementing surveillance-response in consideration of the spatiotemporal nature of cases as well as logistical and financial constraints of the respective programmes. Conclusions: Geospatial systems developed to guide Pacific Island malaria elimination demonstrate the application of a high resolution SDSS-based approach to support key elements of surveillance-response including understanding epidemiological variation within target areas, implementing appropriate foci-specific targeted response, and consideration of logistical constraints and costs. Keywords: Malaria elimination, Surveillance, Geographic information systems (GIS), Spatial decision support systems (SDSS), Mapping
* Correspondence: [email protected] 1University of Queensland, Infectious Disease Epidemiology Unit, School of Population Health, Brisbane, Australia Full list of author information is available at the end of the article
© 2013 Kelly et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Kelly et al. Malaria Journal 2013, 12:108 Page 2 of 14 http://www.malariajournal.com/content/12/1/108
Background (GIS), global positioning systems (GPS), and high per- As efforts to combat the global burden of malaria con- formance mobile computing and telecommunication de- tinue, there is a renewed focus on the need to vices, is likely to present considerable benefits. Whilst strengthen surveillance throughout all phases of intensi- the potential of these geospatial resources is recognized, fied malaria control and elimination [1-7]. This renewed the development and practical implementation of mod- emphasis on surveillance and its ultimate role in identi- ern, rapid fine-scale mapping tools at a resolution de- fying and rapidly tackling remaining transmission reser- tailed enough to support surveillance-response requires voirs by adequate integrated response package is further research [11]. highlighted by the World Health Organization (WHO) Spatial decision support systems (SDSS) are computer- Global Malaria Programme “T3: Test. Treat. Track” ini- ized management systems designed to address complex tiative and the publication of operational manuals to geographic or spatial problems [23]. These systems are support surveillance-response approaches for malaria interactive and generally based around a GIS platform control and elimination [8-10]. that integrates a database management system, graphical As countries reduce transmission and progressively map interface, tabular reporting and expert knowledge move from intensified control to pre-elimination, elimin- of the user [23,24]. An SDSS provides a potential plat- ation and eventually the prevention of reintroduction, form to process spatial information that is required to surveillance practices are required to evolve into a more support surveillance-response decision making for mal- focused intervention that incorporates passive, active and aria elimination [22]. reactive case detection, and appropriate response mea- Progressive malaria elimination campaigns are cur- sures [11,12]. For malaria elimination, where the goal is rently being pursued by the governments of Solomon to halt localized transmission, the objective of surveil- Islands and Vanuatu, with support from the Australian lance systems is to rapidly detect, classify and attack all Agency for International Development (AusAID) Pacific infection foci (both symptomatic and asymptomatic) to Malaria Initiative (PacMI), World Health Organization ensure all cases are treated before the occurrence of sec- (WHO) and other partners. GIS-based SDSS approaches ondary cases and perpetuation of local transmission have been developed, validated and adopted by the mal- [10,13]. A detailed understanding of the local micro- aria programmes in both countries to strengthen geo- epidemiological situation within target areas is essential graphical reconnaissance (GR) [25] and to facilitate the to actively identify transmission foci and implement ap- implementation of vector control interventions includ- propriate responses [14-17]. As transmission declines ing long-lasting insecticidal net (LLIN) distribution and and locally acquired cases approach zero, sound vigilance focal indoor residual spraying (IRS) [22,26]. As these also becomes essential to monitor outbreak and import- programmes progress, there is an increasing need to ation risk [18,19]. focus on the development, implementation and strength- Whilst the integral role of surveillance in malaria elim- ening of effective surveillance-response to support elim- ination is recognized, significant challenges remain. ination in the region [27,28]. This study focuses on the Existing health systems often struggle to support the high continued development of SDSS-based operational level of cohesion, precision and responsiveness required tools to support surveillance-response in both coun- to address the increased complexities of malaria elimin- tries. Specific aims of this study were to develop a ation [4]. There is a call to explore novel technologies, geospatial surveillance tool to automatically locate and approaches and decision-making tools to support the map the distribution of reported confirmed malaria effective implementation and integration of surveillance- cases by household; and explore the effectiveness of an response into health systems managing intensified mal- SDSS-based approach in supporting the rapid classifi- aria control and elimination campaigns [1-4,11,20,21]. cation of identified active transmission foci to stra- Since the global malaria eradication period of the tegically guide targeted responses that can be applied 1950s–1960s, mapping and geographical reconnaissance within malaria elimination zones in the Asia-Pacific re- (GR) has been a component of field-based vector control gion and globally. operations. However, limitations have included access to spatial data and the time consuming nature of manual Methods hand-drawn cartographic, data collection and record Study areas keeping practices [11,22]. Surveillance-response ap- SDSS-based surveillance systems have been established in proaches for malaria elimination is inherently geographic the malaria elimination provinces of Isabel and Temotu, in its requirement to detect cases, locate transmission Solomon Islands and in Tafea province, Vanuatu (Figure 1). foci and target appropriate responses accordingly. Thus, Data on malaria cases collected in the three provinces mapping, including the use of contemporary geospatial throughout 2011 were used as the basis of this study. technologies such as geographic information systems Approval for this study was provided by the Ministries of Kelly et al. Malaria Journal 2013, 12:108 Page 3 of 14 http://www.malariajournal.com/content/12/1/108
Figure 1 General location map of Solomon Islands and Vanuatu elimination provinces.
Health (MoH) in each country, who formally requested enumerated and geo-referenced household data col- in-country technical support to assist in the development lected during detailed GR surveys conducted between and implementation of a practical and easy-to-use SDSS- 2008 and 2010 in all elimination provinces [25]. Specific based malaria surveillance system to support progressive components of the surveillance-response SDSS frame- malaria elimination. Ethical approval was not sought dur- work included the development of interactive GIS-based ing this study because the application and procedures de- applications to map and define health facility catchment veloped are considered part of routine operational areas; automatically map confirmed malaria cases by activities of the national malaria programmes in each household; automatically classify and map transmission country, with all data collected and managed as per confi- foci based on the spatiotemporal distribution of cases dentiality requirements of the Solomon Islands and and highlight “current areas of interest” to conduct re- Vanuatu Ministries of Health. Data collection activities and active case detection and response activities. Table 1 SDSS-based surveillance operations were conducted by provides a summary of the technical SDSS applications provincial vector borne diseases control programme developed and their practical function for surveillance- (VBDCP) surveillance, monitoring and evaluation (SM&E) response. A screenshot of the custom SDSS user inter- officers as part of their routine activities, with technical as- face is provided as Additional file 1. sistance provided by the Pacific Malaria Initiative Support Following consultation with national and provincial Centre (PacMISC) and WHO. VBDCP partners in Solomon Islands and Vanuatu, forms were developed to communicate essential data Development of the spatial decision support system on all confirmed malaria cases from health facilities for Custom applications were built into the existing provin- entry into the provincial-level SDSS. A simple database cial SDSS used to support general topographic map- and data entry template was developed in Microsoft ping, GR and vector control intervention management Access (Microsoft Corporation, Redmond, WA, USA). in the elimination provinces [25,26], using MapInfo An automated link between the surveillance database Professional and MapBasic (Pitney Bowes Software Inc., and SDSS was developed to make reported case data NY, USA) GIS software. The existing system included available in the surveillance-response system in real Kelly et al. Malaria Journal 2013, 12:108 Page 4 of 14 http://www.malariajournal.com/content/12/1/108
Table 1 Summary of the technical components of the malaria elimination geospatial surveillance-response SDSS SDSS component Technical function and application for surveillance response View / Map Positive Malaria Case Data Automated synchronization between rapid case surveillance reporting database and GIS-based SDSS to geo-reference positive reported cases by household Positive case household mapping application to instantly view the spatial distribution of all geo-referenced positive reported cases via a GIS-based map Dialog window to enable SDSS-user to develop customized GIS maps of specific case data e.g. maps by species type, date range, and transmission mode to support epidemiological investigation Option to search, locate and access individual case data via a GIS-based map interface to support individual case investigation Active Transmission Focus Mapping GIS-based mapping application to automatically update, re-classify and map active transmission foci based on incoming reported positive cases; and epidemiological, entomological and environmental data Current Area of Interest Focus Mapping Interactive dialog window to enable SDSS-user to define the parameters to trigger current area of interests (AOI) i.e. The number of positive cases reported in a defined geographical radius of each other within a set time period Automated mapping application to generate current AOI maps to highlight current geographic locations to concentrate response actions based on user defined parameters Interactive map-based application to enable the SDSS-user to modify individual AOI regions automatically generated to adjust geographic areas based on local knowledge Automated GIS queries to extract specific data to support rapid response interventions within current AOI including general household and population summaries of all current AOIs; and historical case data, detailed household listings, and known larval site data by individual AOI Health Facility Catchment Definition Interactive map-based application to draw and define health facility catchment boundaries via a graphical and Mapping map interface Automated query to extract enumerated household lists and population data for individual catchment areas to support the geo-referencing at positive cases upon diagnosis and investigation Automated queries to produce general health catchmentsummariesbytotalpopulation and households; and detailed individual health catchment summaries by village, population, age breakdown and households time. A screenshot of the surveillance reporting data and date of notification of each other to trigger an AOI entry form template is also provided as Additional file 2. within the customized geospatial surveillance-response SDSS. As cases were uploaded into the SDSS, buffer re- Development of Isabel Province surveillance-response gions automatically mapped the geographic areas meet- conceptual framework ing the minimum AOI requirements to highlight areas Detailed consultations were held between national and requiring a follow-up response. Associated household, provincial VBDCP personnel in Isabel province to de- population and transmission data of each AOI were also velop a malaria elimination surveillance-response con- automatically generated to support reactive response ceptual framework (Figure 2). Key elements discussed measures in these areas. Initial AOI parameters in Isabel included the role of the SDSS in supporting rapid case were set at two or more cases within 2 km, reported reporting using passive and routine active case detection within the last 90 days. methods, the identification of current areas of interest Following consultations held in Isabel, automated GIS (AOI) to conduct re-active focal screening and treatment queries were integrated into the SDSS to automatically (FSAT) based on reported cases and the automated clas- classify and map active (and residual non-active) trans- sification and mapping of transmission foci to guide ap- mission foci based on localized geographic, epidemio- propriate response measures in defined AOI. logical, and entomological factors. Table 2 provides a To automate the identification of current AOI regions, summary of the parameters used to automatically classify GIS-based queries were developed and incorporated into and map these transmission foci in the SDSS and the as- the SDSS. AOI region parameters were based on inter- sociated response interventions selected by the VBDCP. active user-defined parameters designed to reflect the local spatiotemporal relationship of cases and transmis- Rapid case reporting sion, the local vector flight range [29,30], and the oper- Protocols developed for Isabel, Temotu and Tafea prov- ational capacity of the VBDCP office to conduct response inces specified that all positive cases, confirmed by in relation to current case-loads. Provincial SM&E offi- Giemsa-stained blood smear examination or ICT Malaria cers were able to define the minimum number of positive Combo Cassette Test (ICT diagnostics) rapid diagnostic cases reported within a defined geographical distance test (RDT), were to be reported to the provincial VBDCP Kelly et al. Malaria Journal 2013, 12:108 Page 5 of 14 http://www.malariajournal.com/content/12/1/108
Figure 2 Spatial decision support system based malaria elimination surveillance-response framework. This schematic illustrates the SDSS- based framework adopted specifically for Isabel Province, Solomon Islands to guide surveillance-response operations. Key elements of the framework include: 1. Case detection and rapid notification via both passive and pro-active case detection methods; 2. Epidemiological case investigation and classification through automatic case mapping, and treatment and investigation of individual cases; 3. Focus investigation and classification via targeted re-active case detection and entomological assessment, historical case review, and automated GIS-based queries within the SDSS to classify and map transmission foci and generate response areas of interest (AOI); and 4. Focus specific targeted action based on identified AOI geographic areas and the classification of associated transmission foci (see Table 2). Kelly et al. Malaria Journal 2013, 12:108 Page 6 of 14 http://www.malariajournal.com/content/12/1/108
Table 2 Active and residual non-active transmission foci classification parameters and nominated response interventions adopted in Isabel province surveillance-response SDSS Focus type Characteristics SDSS classification parameters Nominated response interventions Residual active - Transmission occurring in an area All areas within a 3km geographical range Focal screening and treatment of ongoing transmission of a positive case in an area that has had Larval source management - Effectively controlled with major one or more additional positive recorded Updated geographical reconnaissance reductions recorded after interventions case within the last 3 months Community Awareness New active - Transmission occurring in area that All areas within a 3km geographical range Focal screening and treatment has had transmission for less than of a positive case in an area of known Focal indoor residual spraying 2 years or has never had local transmission but has not had an additional Long lasting insecticidal net assessment transmission reported case within 3 months and re-distribution as required -1st Degree: Only introduced cases Larval source management present; 2nd Degree: Secondary and Updated geographical reconnaissance indigenous cases present Community Awareness New potential - Isolated imported, induced or relapse All areas within a 3km geographical range Focal screening and treatment cases occurring only of an imported positive case in a known Focal indoor residual spraying - Receptive area with no transmission receptive area that has not had transmission Long lasting insecticidal net assessment for at least 2 years for a period of 2 years and re-distribution as required Larval source management Updated geographical reconnaissance Community Awareness Residual Non-active - History of local transmission however All areas within a 3km geographical range Focal screening and treatment not within the last 2 years of a relapsed case in a known receptive Direct observed treatment and case - Relapses or delayed primary infections area that has not had transmission for a follow-up with P. vivax or treatment failure of period of 2 years Updated geographical reconnaissance infection before transmission Community Awareness offices within 24 hours by mobile phone (if available) or procedures (SOPs) were also developed to guide reporting radio communication. Case data were updated into procedures and support technical operations. Briefings surveillance-response SDSS by provincial SM&E officers with health facility officers were conducted on rapid case using the surveillance reporting database form. The reporting procedures. Support was also provided to provin- spatial and temporal distributions of positive malaria cial surveillance officers through regular consultation, both cases were then viewed and monitored by malaria sur- remotely and during routine provincial visits. veillance staff using the SDSS map interface. Case distri- bution maps and tabular summaries were developed and reported to the respective national malaria offices as part Evaluation and validation of the spatial decision support of routine monthly reporting procedures. system To support the rapid reporting of positive cases, Confirmed positive case data reported throughout 2011 health facility catchment areas were mapped in the were used to evaluate the geospatial surveillance- SDSS during planning sessions by health facility staff response SDSS. Local and imported case data summaries, and provincial VBDCP SM&E officers, using the case household distribution maps, AOI maps and associ- existing geo-referenced GR household data. Hardcopy ated data summaries were automatically produced for household location maps and associated lists showing Isabel, Temotu and Tafea provinces using the customized household number, family name, village and house- SDSS. Local cases were defined as infections that were hold demographics were then issued to all health fa- classified as having been acquired within each respective cilities. These data were used by health facility elimination province, with imported cases classified as an officers during diagnosis and case investigation to re- infection acquired outside of the individual respective port the suspected site of transmission to the provin- elimination province. cial VBDCP office. Hardcopy and digital household Simulations based on varying AOI criteria were also location maps and associated lists were also stored at run to calculate the number of current AOIs triggered the provincial level and could be accessed by provin- throughout 2011 in Isabel, Temotu and Tafea and the cial VBDCP staff in the customized SDSS. proportion of positive cases, households and population highlighted in the identified areas. A sensitivity analysis for the AOI criteria was conducted using a simulation Training approach, with the criteria varied as follows: (i) two or Introductory 2-day training sessions were held in Isabel, more cases within a 1 km radius of each other within Temotu and Tafea province respectively, covering the basic 90 days (2c1km90d); (ii) two or more cases within a operation of the customized SDSS. Standard operating 2 km radius of each other within 90 days (2c2km90d); Kelly et al. Malaria Journal 2013, 12:108 Page 7 of 14 http://www.malariajournal.com/content/12/1/108
Table 3 Breakdown of 2011 case species type and suspected mode of transmission Elimination P. vivax P. falciparum Mixed Total Total area Cases Local Imported1 Local Imported1 Local Imported1 Local Imported1 Isabel 11 9 4 1 1 0 16 10 26 Temotu 125 5 8 2 1 0 134 7 141 Tafea 7 4 4 1 0 0 11 5 16 Total 143 18 16 4 2 0 161 22 183 1 Imported case is defined as an infection classified as being acquired outside of the respective elimination province. and (iii) two or more cases within a 3 km radius of each response SDSS throughout 2011. Of these confirmed cases, other within 90 days (2c3km90d). 26 were reported in Isabel Province and 141 in Temotu The time taken to report and update cases into the Province, Solomon Islands; and 16 in Tafea Province, SDSS from the date of diagnosis was recorded in Isabel Vanuatu. Suspected local transmission accounted for province to evaluate the timeliness of case reporting. 61.6%, 95.0% and 68.8% of all reported cases in Isabel, The proportion of confirmed positive cases successfully Temotu and Tafea respectively. Plasmodium vivax was the geo-referenced by households were also examined in the dominant species reported in all elimination provinces ac- three elimination provinces to assess the effectiveness of counting for 76.9%, 92.2% and 68.8% in Isabel, Temotu the geospatial surveillance-response SDSS. and Tafea respectively. Table 3 provides a breakdown of positive cases by species type and suspected mode of trans- Results mission by province in 2011. Case detection and mapping Figures 3, 4 and 5 illustrate maps of cases generated A total of 183 confirmed positive malaria cases were for Isabel, Temotu and Tafea by species in 2011. There reported and recorded in the three provincial surveillance- were no technical or operational issues reported in any
Figure 3 Malaria case distribution map, Isabel Province, Solomon Islands, 2011. Kelly et al. Malaria Journal 2013, 12:108 Page 8 of 14 http://www.malariajournal.com/content/12/1/108
elimination province that impeded the day-to-day gener- Comparison of “Area of Interest” parameters ation of SDSS-based case distribution maps by the pro- Table 4 provides a detailed summary of the AOI simula- vincial surveillance teams. tion data for Isabel, Temotu and Tafea provinces com- Transmission in Isabel Province was concentrated in paring variations between geographic radius parameter the residual active areas of the south-eastern populated between 1 km, 2 km and 3 km. Figure 7 illustrates the communities of the Tatamba region on the main island total proportion of positive reported cases per month as well as the provincial centre of Buala (Figure 3). against detected AOI regions for each AOI simulation. Isolated imported cases were also reported in logging A relatively steady increase in the proportion of cases camps along the north-western coastline of Isabel, detected in Isabel was observed with the 1 km, 2 km, detected both passively and as part of routine active case and 3 km AOI criteria, with 69.2%, 80.8%, and 92.3% re- detection (ACD). Transmission in Temotu Province was spectively. In Temotu, no difference was recorded in the predominately concentrated in the provincial capital and proportion of cases (76.0%) detected based on the 1 km, main entry port of Lata on the main island of Santa Cruz, 2 km, and 3 km AOI criteria. Whilst no difference in the with additional reported cases reported in active trans- proportion of cases was recorded, an increase of 1298 mission areas of the north coast of Santa Cruz (Figure 4). population (6.0% of the total provincial population) and In Tafea, transmission was somewhat sporadic, with the 311 households (6.0% of total provincial households) was majority of cases reported on the main island of Tanna, identified between the 1 km and 3 km AOI regions. In particularly around the provincial capital of Lenakel Tafea, a relatively small proportion of cases (18.8%) were (Figure 5). detected based on the 1 km geographic radius parameter, Figure 6 illustrates an AOI map generated in Isabel increasing to only 50% of total cases detected when the Province on 31st December 2011 based on the default AOI radius was increased to 3 km. Based on the 1 km and AOI parameters of 2 or more positive cases reported 2 km parameters, only January and February recorded AOI within a geographic radius of 2 km in the last 90 days. regions in Tafea during 2011.
Figure 4 Malaria case distribution map, Temotu Province, Solomon Islands, 2011. Kelly et al. Malaria Journal 2013, 12:108 Page 9 of 14 http://www.malariajournal.com/content/12/1/108
Figure 5 Malaria case distribution map, Tafea Province, Vanuatu, 2011.
Timeliness of reporting of cases Discussion Table 5 provides a breakdown of the recorded times This paper demonstrates the use of geospatial technol- taken for the Isabel provincial office to receive notifica- ogy and processes as part of integrated surveillance- tion of cases from the health facility and updated into response approaches for malaria elimination in Solomon the surveillance-response SDSS in 2011. Of the total Islands and Vanuatu. In all three elimination areas, the recorded cases, 46.2% of cases were received within provincial VBDCP office serves as the focal point for the 24 hours, as required in the rapid reporting protocol. coordination and implementation of malaria elimination activities. Development of the SDSS-based systems focused Geo-referencing of reported positive cases by household on providing a practical, user friendly operational tool at A high proportion of positive cases were successfully the provincial level to support detailed surveillance- geo-referenced by household in 2011 with 100% of posi- response, based on routinely collected programme data tive cases in Isabel, 78.0% in Temotu, and 93.8% in Tafea and existing health systems currently in place. (Table 6). Common reasons that cases were not geo- Logistical constraints associated with poor transport referenced by household included: confusion among and communications infrastructure, limited resources health facility workers about how to assign household and isolated communities present significant operational numbers to visitors, the construction of new houses challenges for the effective delivery of essential services without a geo-referenced household number, and a lack in Pacific Island settings where malaria elimination is of understanding on the importance of reporting house- currently being pursued. A need for effective operational hold numbers as part of the elimination surveillance- tools to accurately identify transmission foci, and support response system. Of the cases that were not successfully the efficient targeting of response interventions and geo-referenced by household, 100% were successfully timely allocation of associated resources to designated geo-referenced at the village level by provincial surveil- geographic locations is crucial to ensure local transmis- lance officers. sion can be effectively halted. The ability to automatically Kelly et al. Malaria Journal 2013, 12:108 Page 10 of 14 http://www.malariajournal.com/content/12/1/108
Figure 6 Current Area of Interest (AOI) map, Isabel Province, Solomon Islands, 31/12/2012. This map illustrates a “Current AOI map” generated using the Isabel Province surveillance-response SDSS for 31st December, 2011. The AOI area illustrated was automatically generated in the SDSS based on the AOI defined parameters of two or more malaria cases detected within a two kilometre radius of each other within the last 90 days of the current date (31/12/2012). Key elements of the automated map include: (i) the designation of the geographic AOI area to conduct response; (ii) the type of transmission focus the AOI is located within to guide the selection of nominated response interventions; (iii) the illustration and generation of household and population data within the AOI to support rapid resource allocation, costing, and field implementation of nominated interventions.
map both passively and actively detected cases in the support the implementation of interventions within SDSS provides an effective mechanism to visualize the identified transmission foci. distribution and pattern of malaria transmission in these The data presented from 2011 indicate different stages elimination settings at high resolution and in relative of progress in each of the elimination provinces. With real-time (i.e. as soon as cases are reported). As Figure 6 the limited resources, available finances and logistical indicates, a SDSS-based framework also allows for the constraints characterising these areas, the total number application of powerful GIS capabilities to query the spa- of reported cases occurring in each elimination zone has tiotemporal characteristics of current and historical case operational implications with regard to the current na- data in relation to local geographic settings. Such a ture of surveillance-response approaches that each framework enables programme managers to locate indi- programme can effectively carry out. In Temotu prov- vidual positive cases at a detailed household level; iden- ince, where total reported cases were highest, targeted tify, select and map priority AOI regions; guide the responses based on individual cases is not yet operation- selection of appropriate focus-specific response inter- ally feasible. Whilst transmission is comparatively high ventions; and extract detailed supplementary data (such in Temotu, the positive case household map (Figure 4) as population summaries and household listings). Ac- illustrates the largely clustered and heterogeneous nature cess to such detailed data supports swift and effective of transmission in the province. This clustering is also decision-making in peripheral and remote areas includ- indicated in the AOI simulation data (Table 4, Figure 7). ing the rapid preparation of budgets, allocation of re- In the context of managing surveillance-response, these quired resources and mobilization of personnel to data suggest that re-active case detection and response Kelly et al. Malaria Journal 2013, 12:108 Page 11 of 14 http://www.malariajournal.com/content/12/1/108
Table 4 Proportion of cases detected and associated response system presented in this paper is its ability to population and households located within SDSS provide surveillance officers with a tool to visualize the generated areas of interest (AOI) of varying radius variation in current transmission across the entire elim- Isabel Temotu Tafea Total ination zone and adjust AOI regions according to the Total Cases 26 141 16 183 local situation to rapidly extract detailed information, in- Pop^ 30167 21552 38635 90354 cluding costs, to guide appropriate integrated response HHs^^ 6410 5221 8850 20481 packages in various transmission settings. 2c1km90d* Cases 69.23% 75.89% 18.75% 69.95% Health systems challenges in Solomon Islands and Vanuatu currently pose constraints to the effectiveness [18] [107] [3] [128] of the SDSS based surveillance-response systems in the Pop 11.96% 23.96% 5.12% 11.90% three selected elimination provinces. As the SDSS sys- [3608] [5163] [1980] [10751] tem relies upon on effective passive case detection (illus- HHs 10.66% 23.64% 5.14% 11.58% trated in Figure 2), the performance of this system is [683] [1234] [455] [2372] dependent upon key health system components, particu- 2c2km90d** Cases 80.77% 75.89% 31.25% 72.13% larly at the health facility and community levels. These [21] [107] [5] [132] challenges include the need to strengthen community Pop 23.34% 26.88% 17.90% 21.86% engagement and participatory surveillance to encourage [7040] [5793] [6917] [19750] early treatment seeking behaviour and community level vigilance [27,28]; accurate diagnostics; timely and effect- HHs 22.40% 26.49% 17.67% 21.40% ive reporting of cases from the health facility to provin- [1436] [1383] [1564] [4383] cial level via suitable communication channels; and 2c3km90d*** Cases 92.31% 75.89% 50.00% 75.41% robust pro-active case detection and increased vigilance [24] [107] [8] [138] in high risk priority areas such as known populations of Pop 30.47% 29.98% 31.76% 30.91% high mobility (e.g. logging and mining camps) and com- [9193] [6461] [12270] [27924] mon entry points (e.g. sea and air ports). Previous malariometric surveys conducted in the HHs 29.75% 29.59% 31.94% 30.66% elimination provinces have revealed a high burden of [1907] [1545] [2827] [6279] asymptomatic malaria infections of low parasite density (^Pop: Population; ^^HHs: Households; *2c1km90d AOI criteria: 2 or more cases [27,31,32]. Use of sensitive field-based molecular within 1 km radius of each other within 90 days; **2c2km90d AOI criteria: 2 or more cases within 2 km radius of each other within 90 days; ***2c3km90d AOI methods will likely be required to effectively detect criteria: 2 or more cases within 3 km radius of each other within 90 days). these low level infections [31]. As these operations are implemented, reported cases will need to be integrated interventions could initially be targeted to a smaller geo- into the SDSS-based surveillance-response framework graphic radius (e.g. 1 km) in these transmission foci, to track progress and continue to provide appropriate utilising less resources and finances to seek out and treat response interventions. additional asymptomatic cases and efficiently attack The effectiveness of the SDSS surveillance-response transmission. In contrast, in Tafea province, where total system is also highly dependent upon the timely and ac- positive cases reported were comparatively lower, the curate reporting of cases at the health facility level. As spatiotemporal distribution of cases was more sporadic reflected in Table 5, significant challenges remain to en- and dispersed, with only a small proportion of cases sure cases are immediately reported to the provincial detected during the AOI simulations throughout 2011. level to update the SDSS surveillance-response system. These data suggest the readiness and need to adjust AOI Current plans to utilize digital mobile communication parameters in Tafea to trigger response interventions technologies to support rapid case reporting from the based on single cases to maximize the operational impact health facility in the elimination provinces are underway. of the implemented surveillance-response interventions The capacity of health facilities to geo-reference data to support the elimination of remaining parasite reser- and correctly classify cases also impacts on the ability of voirs and prevent re-introduction of local transmission. the SDSS to automatically map the distribution of cases Complexities associated with the heterogeneous nature and accurately classify transmission foci, particularly as of malaria transmission (both spatial and temporal) and the number of cases detected heads towards zero. Pol- additional factors such as the risk of re-introduction into icies to conduct updated and re-active GR mapping as non-active receptive areas through imported cases, required and standard procedures to support case inves- present management challenges for elimination that re- tigation and classification have been incorporated into quire a multifaceted approach to decision-making and the surveillance-response framework to mitigate these response. An advantage of the SDSS-based surveillance- particular operational challenges. In conjunction with Kelly et al. Malaria Journal 2013, 12:108 Page 12 of 14 http://www.malariajournal.com/content/12/1/108
Figure 7 Proportion of positive case per month against detected AOI regions of varying geographic radius. Figure illustrates the temporal distribution of cases in each elimination province in relation to area of interests (AOI) of varying geographic radius 1 km, 2 km, 3 km. Figure shows the total number of cases reported by month and the proportion of those reported cases located within an active AOI geographical region based on the set parameters defined within the SDSS. these technological and procedural mechanisms, further areas for response and guide the selection of appropriate emphasis on supporting health worker performance and integrated interventions, further work is still required reinforcing the importance of the immediate and accur- in all three provinces with regard to the practical imple- ate reporting of geo-referenced positive cases at the mentation and reporting of targeted responses interven- health facility level as part of an effective surveillance- tions, including reactive case detection, in areas response framework for malaria elimination is essential. identified by the SDSS-based surveillance-response Currently all case detection in the elimination prov- system. As this paper largely focuses on surveillance inces of Solomon Islands and Vanuatu occurs within the and the identification of target response areas, the ef- public sector. In areas where both public and private fective identification and monitoring of appropriate sectors operate concurrently, additional effort would be foci-specific interventions and adequate response times required to capture and include in the surveillance- remain future research questions. response framework malaria cases detected in patients seeking health care through the private sector. Conclusion Adequate response capacity is essential to any effective This study has illustrated the application of SDSS tech- surveillance package [13]. Whilst the SDSS surveillance- nology to support high-resolution surveillance-response response system provides a framework to identify priority for malaria elimination in remote Pacific Island settings. This study has shown how an SDSS based approach to Table 5 Time taken to report cases from health facility to Isabel provincial office during 2011 Table 6 Proportion of 2011 reported positive malaria Time taken for provincial office to Total cases Proportion of cases successfully geo-located at the household level receive case notification cases Elimination Total Cases Percent (%) Reported within 24 hours 12 46.15% area cases geo-located geo-located Reported within 72 hours 0 0% Isabel 26 26 100.00 Reported within 1 week 9 34.62% Temotu 141 110 78.01 Reported within 1 month 4 15.38% Tafea 16 15 93.75 Reported after 1 month 1 3.85% Total 183 151 82.51 Kelly et al. Malaria Journal 2013, 12:108 Page 13 of 14 http://www.malariajournal.com/content/12/1/108
surveillance-response can utilize GIS queries to support Author details 1 the identification and mapping of malaria cases at a de- University of Queensland, Infectious Disease Epidemiology Unit, School of Population Health, Brisbane, Australia. 2Ministry of Health and Medical tailed household level, visualize and classify active trans- Services, Vector Borne Diseases Control Programme, Honiara, Solomon mission foci, detect priority geographic areas to conduct Islands. 3Ministry of Health, Vector Borne Diseases Control Programme, Port 4 follow-up activities, and automatically extract detailed Vila, Vanuatu. Griffith University, School of Public Health, Brisbane, Australia. 5University of Hawaii, John A. Burns School of Medicine, Honolulu, USA. data to support the rapid mobilization of appropriate re- 6University of New South Wales, The Kirby Institute for infection and sponse interventions. When integrated into existing immunity in society, Sydney, Australia. 7Swiss Tropical & Public Health 8 9 health systems, an SDSS framework provides programme Institute, Basel, Switzerland. University of Basel, Basel, Switzerland. World Health Organization, Western Pacific Region, Manila, Philippines. managers with an effective and flexible operational tool to actively understand micro and meso-epidemiological Received: 23 November 2012 Accepted: 14 March 2013 variations within an elimination area and respond ac- Published: 21 March 2013 cordingly. SDSS-based geospatial surveillance-response systems currently remain in operation in all three elimin- References ation provinces in the Solomon Islands and Vanuatu. 1. Feachem RGA, Phillips AA, Targett GA, Snow RW: Call to action: priorities – Further refinement and validation of these systems and for malaria elimination. Lancet 2010, 376:1517 1521. 2. Tanner M, de Savigny D: Malaria eradication back on the table. Bull World their extension to include a costing assessment of the Health Organ 2008, 86:82. surveillance system and associated response packages are 3. Alonso PL, Brown G, Arevalo-Herrera M, Binka F, Chitnis C, Collins F, currently continuing, as well as additional applications Doumbo OK, Greenwood B, Hall BF, Levine MM, Mendis K, Newman RD, Plowe CV, Rodriguez MH, Sinden R, Slutsker L, Tanner M: A research being developed to support intensified malaria control agenda to underpin malaria eradication. PLoS Med 2011, 8:e1000406. and broader disease surveillance within the region. 4. The malERA Consultative Group on Health Systems and Operational Research: A research agenda for malaria eradication: health systems and operational research. PLoS Med 2011, 8:e1000397. Additional files 5. Hay SI, Smith DL, Snow RW: Measuring malaria endemicity from intense to interrupted transmission. Lancet Infect Dis 2008, 8:369–378. 6. Najera JA, Gonzalez-Silva M, Alonso PL: Some lessons for the future from Additional file 1: Screenshot of the Isabel Province surveillance- the Global Malaria Eradication Programme (1955–1969). PLoS Med 2011, response spatial decision support system user interface. 8:e1000412. Additional file 2: Screenshot of the Temotu Province rapid case 7. Wernsdorfer W, Hay SI, Shanks GD: Learning from history. In Shrinking the surveillance reporting digital data entry form template. Malaria Map: A Prospectus on Malaria Elimination. Edited by Feachem RGA, Phillips AA, Targett GA. San Francisco: The Global Health Group, UCSF Global Health Sciences; 2009. Competing interests 8. WHO: Test. Treat. Track. Scaling up diagnostic testing, treatment and The authors declare that they have no competing interests. surveillance for malaria. Geneva, Switzerland: World Health Organization Global Malaria Programme; 2012. 9. WHO: Disease surveillance for malaria control an operational manual. Geneva, ’ Authors contributions Switzerland: Roll Back Malaria Partnership; 2012:84. The study was conceived and designed by GK, with support from ACAC, MT, 10. WHO: Disease surveillance for malaria elimination an operational manual. AV, LV, AB & GT. Field training was conducted by GK, JS, EH & WD. Field Geneva, Switzerland: Roll Back Malaria Partnership; 2012:64. research operations were coordinated by EH, WD, WB, JN, GK & JS. ACAC 11. The malEra Consultative Group on Monitoring, Evaluation and Surveillance: provided guidance on scientific aspects of the study and provided detailed A research agenda for malaria eradication: monitoring, evaluation, and commentary on manuscript drafts. Additional scientific and technical surveillance. PLoS Med 2011, 8:e1000400. guidance was provided by MT, AV, LV, KP & HB. Manuscript drafting was 12. Mendis K, Rietveld A, Warsame M, Bosman A, Greenwood B, Wernsdorfer carried out by GK with support from all authors. All authors read and WH: From malaria control to eradication: The WHO perspective. Trop Med approved the final manuscript. Int Health 2009, 14:802–809. 13. Moonen B, Cohen JM, Snow RW, Slutsker L, Drakeley C, Smith DL, Acknowledgements Abeyasinghe RR, Rodriguez MH, Maharaj R, Tanner M, Targett G: We would like to thank the hard work of the individuals from the Solomon Operational strategies to achieve and maintain malaria elimination. Islands and Vanuatu National Vector Borne Disease Control Programmes and Lancet 2010, 376:1592–1603. Ministry of Health who assisted with the fieldwork and data collection 14. Bousema T, Griffin JT, Sauerwein RW, Smith DL, Churcher TS, Takken W, including all additional provincial based health facility and malaria Ghani A, Drakeley C, Gosling R: Hitting hotspots: spatial targeting of surveillance officers in Isabel, Temotu and Tafea, especially Watson Hevalao, malaria for control and elimination. PLoS Med 2012, 9:e1001165. Landry Losi, Rachael Seni, Andrew Newa, and Robert Raoga (Solomon 15. Greenwood BM: The microepidemiology of malaria and its importance to Islands); and Harry Iata, Rueban Sumaki, James Amon and Aaron Tebi malaria control. Trans R Soc Trop Med Hyg 1989, 83(Suppl):25–29. (Vanuatu). We also thank the ongoing support of the Pacific Malaria Initiative 16. Sabot O, Tulloch J, Basu S, Dyckman W, Moonen B: Getting to Zero. In Support Centre (PacMISC), School of Population Health, University of Shrinking the Malaria Map A Prospectus on Malaria Elimination. Edited by Queensland and the World Health Organization (WHO). Feachem RGA, Phillips AA, Targett GA. San Francisco: The Global Health Finally, we also thank the communities of Isabel, Temotu and Tafea Group, Global Health Sciences, University of California; 2009. provinces for their contribution, cooperation and overwhelming support in 17. WHO: Malaria elimination - A field manual for low and moderate endemic making these operations the success they have been. countries. Geneva: World Health Organization; 2007. A.C.A.C. is supported by a Career Development Award from the Australian 18. Cohen JM, Smith DL, Vallely A, Taleo G, Malefoasi G, Sabot O: Holding the National Health and Medical Research Council (#631619). Line. In Shrinking the Malaria Map A Prospectus on Malaria Elimination. Lasse S. Vestergaard is a staff member of the World Health Organization. The Edited by Feachem RGA, Phillips AA, Targett GA. San Francisco: The Global author alone is responsible for the views expressed in this publication and Health Group, Global Health Sciences, University of California; 2009. they do not necessarily represent the decisions or policies of the World 19. WHO: Guidelines on prevention of the reintroduction of malaria. Cairo: World Health Organization. Health Organization, Regional Office for the Eastern Mediterranean; 2007. 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20. Greenwood BM: Control to elimination: implications for malaria research. Trends Parasitol 2008, 24:449–454. 21. GMAP: The Global Malaria Action Plan. Geneva, Switzerland: Roll Back Malaria Partnership: World Health Organization; 2008. 22. Kelly GC, Tanner M, Vallely A, Clements A: Malaria elimination: moving forward with spatial decision support systems. Trends Parasitol 2012, 28:297–304. 23. Densham DJ: Spatial decision support systems. In Geographical Information Systems Volume 1: Principle. Volume 1. Edited by Maguire DJ, Goodchild MF, Rhind DW. Harlom: Longman Scientific & Technical; 1991:403–412. 24. Keenen PB: Spatial decision support systems. In Decision Making Support Systems Achievements and Challenges for the New Decade. Edited by Morah M, Forgionne G, Gupta J. Hershey PA: Idea Group Publishing; 2003:28–39. 25. Kelly GC, Hii J, Batarii W, Donald W, Hale E, Nausien J, Pontifex S, Vallely A, Tanner M, Clements A: Modern geographical reconnaissance of target populations in malaria elimination zones. Malar J 2010, 9:289. 26. Kelly GC, Moh Seng C, Donald W, Taleo G, Nausien J, Batarii W, Iata H, Tanner M, Vestergaard LS, Clements AC: A spatial decision support system for guiding focal indoor residual spraying interventions in a malaria elimination zone. Geospat Health 2011, 6:21–31. 27. Atkinson JA, Johnson ML, Wijesinghe R, Bobogare A, Losi L, O’Sullivan M, Yamaguchi Y, Kenilorea G, Vallely A, Cheng Q, et al: Operational research to inform a sub-national surveillance intervention for malaria elimination in Solomon Islands. Malar J 2012, 11:101. 28. O’Sullivan M, Kenilorea G, Yamaguchi Y, Bobogare A, Losi L, Atkinson JA, Vallely A, Whittaker M, Tanner M, Wijesinghe R: Malaria elimination in Isabel Province. Solomon Islands: establishing a surveillance-response system to prevent introduction and reintroduction of malaria. Malar J 2011, 10:235. 29. Bugoro H, Iro’ofa C, Mackenzie DO, Apairamo A, Hevalao W, Corcoran S, Bobogare A, Beebe NW, Russell TL, Chen CC, Cooper RD: Changes in vector species composition and current vector biology and behaviour will favour malaria elimination in Santa Isabel Province, Solomon Islands. Malar J 2011, 10:287. 30. Daggy RH: The biology and seasonal cycle of Anopheles farauti on Espiritu Santo, New Hebrides. Ann Entomol Soc America 1945, 38:1–13. 31. Harris I, Sharrock WW, Bain LM, Gray KA, Bobogare A, Boaz L, Lilley K, Krause D, Vallely A, Johnson ML, Gatton ML, Shanks GD, Cheng Q: A large proportion of asymptomatic Plasmodium infections with low and sub-microscopic parasite densities in the low transmission setting of Temotu Province, Solomon Islands: challenges for malaria diagnostics in an elimination setting. Malar J 2010, 9:254. 32. The Pacific Malaria Initiative Survey Group on behalf of the Ministries of Health of Vanuatu and Solomon Islands: Malaria on isolated Melanesian islands prior to the initiation of malaria elimination activities. Malar J 2010, 9:218.
doi:10.1186/1475-2875-12-108 Cite this article as: Kelly et al.: A high-resolution geospatial surveillance- response system for malaria elimination in Solomon Islands and Vanuatu. Malaria Journal 2013 12:108.
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Chapter 6
Spatial decision support systems: a way forward to tackle operational
challenges of malaria elimination – a Pacific experience
6.1 Context
Chapters 3, 4 and 5 each address the role of spatial decision support systems (SDSS) in
meeting specific operational priorities of malaria elimination. Chapter 3 examines the
application of modern geographical reconnaissance (GR) and a SDSS in providing
elimination programmes with the capacity to collect and access detailed and accurate data to
generate useful information, knowledge and evidence to support program implementation.
Chapter 4 describes the role of a SDSS for the delivery and implementation of essential services and interventions at optimal levels of coverage for malaria elimination. Chapter 5 demonstrates the application of a high resolution SDSS-based surveillance-response system to locate individual malaria infections, classify and map active transmission foci, and identify geographic areas of interest to conducted response interventions.
This chapter brings together the individual SDSS components addressed in the previous chapters to present and describe the role of an integrated SDSS for malaria elimination, providing an overall retrospective examination of the SDSS applications developed and implemented to support progressive malaria elimination in Solomon Islands and Vanuatu.
Ongoing SDSS data generated between 2008 and 2013 are updated and presented in this chapter to examine progress made in the two countries and pragmatic elements such as SDSS
86 user-acceptability, operational capacity, technical development issues and limitations are described.
Findings from this chapter are significant because they combine the individual SDSS applications developed to address specific operational priorities of malaria elimination to demonstrate the role of an integrated and cohesive SDSS framework in real-world malaria elimination settings. This chapter identifies the high acceptability and ongoing development, application and expansion of a SDSS framework for malaria elimination and intensified control in Solomon Islands and Vanuatu; and highlights the importance of local program engagement, on-going counterpart relationships and access to support technical networks for sustaining and building SDSS-based operational capacity for malaria elimination.
6.2 Details of Publication
This chapter is currently being prepared for submission to the peer-reviewed journal PLoS
One.
87 Spatial decision support systems: a way forward to tackle operational challenges of malaria elimination – a Pacific experience
Gerard C. Kelly*1, Erick Hale2, Wesley Donald3, Albino Bobogare2, George Taleo3, Andrew
Vallely4, Lasse S. Vestergaard5, Marcel Tanner1,6,7, Archie C.A. Clements1
1 University of Queensland, Infectious Disease Epidemiology Unit, School of Population Health,
Brisbane, Australia
2 Ministry of Health and Medical Services, Vector Borne Diseases Control Program, Honiara,
Solomon Islands
3 Ministry of Health, Vector Borne Diseases Control Program, Port Vila, Vanuatu
4 University of New South Wales, The Kirby Institute for infection and immunity in society,
Sydney, Australia
5 World Health Organization, Western Pacific Region, Manila, Philippines
6 Swiss Tropical & Public Health Institute, Basel, Switzerland
7 University of Basel, Basel, Switzerland
Email: Gerard Kelly* – [email protected]
* Corresponding author
88
Abstract
Problem
Progressive malaria elimination campaigns are underway in Solomon Islands (SI) and
Vanuatu. New tools and approaches are required to meet the increased operational demands and priorities of elimination in remote Pacific Island settings.
Approach
Through collaboration between the SI and Vanuatu National Malaria Programmes and external counterparts, a map-based spatial decision support system (SDSS) framework has been developed to support the transition from malaria control to elimination in provinces targeted for elimination.
Local setting
Four provinces, Temotu and Isabel, SI and Tafea and Torba, Vanuatu have commenced malaria elimination.
Relevant changes
A total of 20,733 households, 55,711 building structures and a population of 91,319 have been recorded and mapped through geographical reconnaissance (GR) procedures and entered into the SDSS-framework in the four provinces. These GR data have been utilised within the map-based SDSS framework to actively support the management of long-lasting insecticidal net (LLIN) distribution and focal indoor residual spraying (IRS) interventions to optimise service delivery, achieving an overall household coverage of 97.5% and 91.7% respectively. Confirmed malaria cases are also mapped by household in the SDSS to actively identify transmission foci, conduct high-resolution surveillance and support rapid targeted response.
Lessons learnt
89 A SDSS framework provides an opportunity for health programmes to utilise modern geo- spatial technology to address key operational priorities of malaria elimination. Engaging local program personnel in the development and implementation of SDSS frameworks, maintaining on-going counterpart relationships, and providing access to support networks are central to sustaining and building operational capacity for malaria and expanded health applications.
90 Box 1: Summary of main lessons learned
1. A spatial decision support system (SDSS) framework provides an opportunity for health programmes to utilise modern geo-spatial technology to address key
operational priorities of malaria elimination, including: the delivery of priority interventions at optimal levels of coverage; an ability to conduct high-resolution surveillance and response; and a capacity to provide detailed and accurate data to generate useful information, knowledge and evidence throughout all facets of
program implementation.
2. Engaging elimination program personnel in the design and development of the SDSS framework; ensuring local surveillance, monitoring and evaluation (SM&E)
officers are the primary focal point for SDSS operation and management within their respective target areas; and emphasising the need for transition from a traditional style of monitoring and evaluation to active surveillance-response based on minimal essential data is integral to the effective implementation and integration of a SDSS
for elimination.
3. The establishment of ongoing working relationships and support networks, both between in-country or inter-regional programme counterparts, as well as external
technical partners, will provide a mechanism for program personnel to access continuous support and mentorship to sustain and build SDSS-based operational capacity for malaria elimination, intensified control and expanded health applications.
91 Background
Global malaria elimination strategies include the simultaneous implementation of aggressive and coherent control measures in high burden areas; progressive elimination from endemic margins inwards; and continued research and development of new tools and approaches to support these goals [1-3]. As countries pursue elimination, health systems are required that support and enhance shifting program priorities through the transition from intensified control to pre-elimination, elimination, and the eventual prevention of reintroduction [4, 5].
Priorities include the need to ensure the effective delivery of scaled-up services and interventions at optimal levels of coverage; the ability to actively identify transmission foci and target appropriate responses swiftly; and the capacity to readily provide detailed and accurate data to generate useful information, knowledge and evidence throughout all facets of program implementation [5-8].
Progressive malaria elimination campaigns are currently underway in the Pacific Island
nations of Solomon Islands (SI) and Vanuatu. Malaria control and elimination efforts in both countries are hindered by dispersed and isolated communities; poor transport and communication infrastructure; and limited human resources. As with many resource-poor malaria-endemic countries, where health systems are often already heavily constrained,
meeting the demands and complexities of malaria elimination present significant challenges that require new operational tools and approaches [5, 6]. This paper brings together evidence
from previous studies that have investigated individual aspects of spatial decision support
system (SDSS) applications implemented to support specific operational priorities of the SI
and Vanuatu malaria elimination programs, to provide an integrated assessment of the overall
development and role of a SDSS framework for malaria elimination.
92 Establishment of a malaria elimination spatial decision support system framework
With assistance from the Australian Agency for International Development (AusAID) Pacific
Malaria Initiative (PacMI), the World Health Organization (WHO) and the Global Fund to
fight AIDS, Tuberculosis and Malaria (GFATM), malaria elimination campaigns were
launched in Temotu Province, SI, and Tafea Province, Vanuatu in 2008. Subsequent campaigns commenced in Isabel Province, SI (2009) and Torba Province, Vanuatu (2012).
At the inception of the elimination campaign, the National Malaria Program (NMP) in each country recognized the limitations of previously established malaria control systems and the need to develop new operational tools to support elimination. Consultation between the SI and Vanuatu NMPs and external technical partners highlighted a need for the development of high-resolution operational tools and supporting information systems. Due to the spatial heterogeneity associated with malaria transmission, and the need to effectively target appropriate interventions [9], the adoption of geographic information system (GIS)
technologies was proposed.
With assistance from the PacMI Support Centre (PacMISC) and WHO, a GIS-based spatial
decision support system (SDSS) was developed in each elimination province. A fundamental
element of a SDSS includes the integration of database systems with graphical map display
and automated reporting and analysis capabilities, designed to enhance decision making and
operational management where a geographic perspective is required [10]. Primary aims of the
SDSS-framework were to provide operational support to address malaria elimination priorities in SI and Vanuatu including: the capacity to generate and readily access useful
programmatic data throughout all elimination phases; the effective delivery of long-lasting
93 insecticidal net (LLIN) and indoor residual spraying (IRS) interventions at optimum levels of
coverage; and to actively conduct high-resolution surveillance-response.
To support the development of the SDSS, collaborative agreements were made between the
Ministry of Health and the Ministry of Lands in both countries to share available GIS data and produce baseline topographic maps. Design and technical development of the SDSS took
place at the provincial level in collaboration with local malaria staff, national surveillance
monitoring and evaluation (SM&E) personnel and PacMISC. Operation of the SDSS was
conducted by nominated provincial-based SM&E officers with support provided from
National SM&E units and external technical partners.
Supporting the operational priorities of malaria elimination
Applications developed within the SDSS-framework to address operational priorities of elimination and associated key markers used to monitor effectiveness are outlined in Table
1A.
Prior to the commencement of malaria elimination, the nominated provinces did not have the resources or capacity to collect and utilize high-resolution data. Modernized geographical reconnaissance (GR) procedures were adapted into the SDSS framework to provide a mechanism for the malaria programs to rapidly collect, map and enumerate baseline household and population data within target areas using integrated global positioning systems
(GPS) and handheld computers [11]. Across the four elimination provinces, an increased total of 20,733 households, 55,711 building structures and a population of 91,319 individuals have been recorded, mapped and entered into the SDSS framework, since the original operations in
2008.
94 Table 1A: Summary of spatial decision support system technical functions and applications developed to support specific operational priorities of malaria elimination in Solomon Islands and Vanuatu
Elimination Programme Individual Components Selected Markers and Specific Spatial Decision Support System (SDSS) Function and Application Operational Priority Addressed Determinants Geographical Field-based Data Collection: Handheld digital GPS devices used to rapidly map households and collect Number of households Capacity to readily Reconnaissance / associated data on population and household structures to support priority interventions. and population provide detailed and Household Mapping Data Backup and Review: Automatic application to backup, update and review GR household data from recorded, mapped and accurate data to Data Collection integrated GPS handheld device into field-based laptop and SDSS system. uploaded into SDSS generate useful Access to and generation of relevant data: (i) Household and demographic data accessed via map-based Malaria Elimination Stakeholder and user information, knowledge user interface, tabular reports. (ii) Production of summaries, reports and maps generated via customised Information and acceptability of SDSS and evidence GIS-based query functions to provide programmatic information, assessment and evaluation throughout all Database System platform phases of elimination. Planning: (i) Operational boundaries delineated via the SDSS map interface to plan and coordinate field activities based on the spatial distribution of households, logistics and topography. (ii) Hardcopy household Assessment of coverage Effective delivery of checklists and maps for each operational area automatically extracted to support field operations. Frontline Intervention and spatial distribution scaled-up services and Implementation: Household checklist data re-entered into the SDSS by provincial SM&E officers and Management of frontline interventions at optimal operational progress assessed through interactive digital maps and automated summaries within the SDSS. (LLIN Distribution and interventions by levels of coverage within Monitoring and Evaluation: (i) Lists and maps of households not serviced during field activities produced Focal IRS) household and target areas and reissued to follow-up teams as a mechanism to ensure optimal intervention coverage. (ii) Final population household status maps and automated report summaries generated to provide intervention coverage reports at the completion of each relevant operation cycle. Case Distribution Mapping: (i) Cases detected via both passive and active detection methods are reported to provincial M&E officer and updated into a rapid case surveillance database and automatically geo- referenced to household location within the elimination SDSS. (ii) Positive case household mapping application to view the spatial distribution cases by species type, date range, and transmission mode to support individual case and epidemiological investigation. Active Transmission Foci Classification and Mapping: Application to automatically update, re-classify and Capacity to rapidly map Ability to actively identify map active transmission foci based on incoming reported positive cases; and epidemiological, malaria cases, active transmission foci and Surveillance and entomological and environmental data. transmission foci and target appropriate Response Targeted Rapid Response: (i) Interactive dialog window to enable SDSS-user to define the parameters to identify target response responses swiftly search for active or recently reported cases and to generate associated area of interest (AOI) radii to areas conduct follow-up response interventions including focal screening and treatment. (ii) SDSS framework developed to identify appropriate interventions specific to the transmission foci classification where response activities are to be conducted. (iii) Automated GIS queries to extract specific data to support rapid response interventions within AOI including general household and population summaries of all current AOIs, historical case data, detailed household listings, and known larval site data.
95
During the malaria control phase, the capacity to monitor and evaluate the delivery of interventions in the elimination provinces was limited largely to regional and village-level coverage estimates due to a lack of available data and tools. As part of the SDSS framework,
GR data were utilised to support the planning, implementation, monitoring and evaluation of
LLIN distribution and focal IRS operations at a household level [12]. Interventions managed within the SDSS framework have reported overall household coverage of 97.5% receiving
LLINs and 91.7% receiving IRS within the respective targeted areas. Coverage was also
mapped and reviewed at the household level within the SDSS to identify locations with poor
service delivery and guide follow-up field operations to ensure maximum optimal coverage
was achieved. Figure 1A provides an example screenshot of the Tafea Province SDSS map
interface used to investigate the spatial distribution of IRS by household, illustrating the
ability for the user to visualise via a map interface households not sprayed.
The ability to actively locate and treat locally-acquired malaria infections and prevent the
resurgence of transmission from imported cases is central to the on-going success of
elimination [7]. Prior to the development of the SDSS, elimination provinces in SI and
Vanuatu had no established mechanism to conduct high-resolution surveillance. To provide
provincial malaria programs with the capacity to actively identify transmission foci and target
appropriate responses, a surveillance-response system has been integrated into the SDSS-
framework [13]. Utilising the enumerated baseline GR data within the SDSS as physical
household addresses, malaria cases identified through both passive and active case detection
measures are automatically located and mapped at a household level. Based on the
spatiotemporal distribution of confirmed cases, automated GIS-based queries are used to
classify and map active transmission foci, locate areas of interest (AOI) to conduct follow-up
96 response, and extract detailed location-specific data to support rapid implementation of response measures. In Isabel Province, where high-resolution surveillance for elimination has been intensified, a total of 108 malaria cases have been detected within a target population of
30,167 between January 2011 and March 2013, and reported in the SDSS-based surveillance- response system. Of these reported cases, 95.4% have been successfully located and mapped at household level, with the remaining 4.6% identified by village. Figure 1B provides an example screenshot of the SDSS-based mapping application used to locate and classify active transmission and highlight AOI regions for targeted rapid response. This figure highlights the ability for program personnel to visualise essential information such as transmission foci classification and AOI data summaries; used to guide decision making with regards to appropriate foci specific response and the assessment of required finances and resources to support rapid mobilisation to the identified areas.
To begin to validate and assess the effectiveness of the SDSS framework as a platform for program officers to readily access detailed, accurate and relevant data, acceptability surveys of the 12 national and provincial based SDSS users, and informal group discussions and post- fieldwork interviews with additional relevant stakeholder have been conducted. Table 1B summarizes the key results of these assessments and indicates a high user-acceptability, an increased ability for program personnel to rapidly access spatial data and maps to support all phases of elimination intervention management, as well as a collective interest to expand
SDSS-based applications.
97
Figure 1: Example screenshots of the map-based SDSS user interface used to support priority frontline intervention management (1A); and high-resolution surveillance and response (1B)
98
Table 1B: Summary of spatial decision support system user acceptability surveys and stakeholder focus group discussions and informal interviews conducted in Solomon
Islands and Vanuatu
(i) Summary of user-acceptability Strongly Strongly Agree Undecided Disagree survey questions Agree Disagree The SDSS provides a useful tool for planning 100% 0% 0% 0% 0% and implementation (12) (0) (0) (0) (0) The SDSS is useful for generating reports 100% 0% 0% 0% 0% and maps (12) (0) (0) (0) (0) The SDSS and mapping applications were 75% 17% 8% 0% 0% easy to understand and use (9) (2) (1) (0) (0) The SDSS is a more effective operational 92% 8% 0% 0% 0% tool than traditional mechanisms (11) (1) (0) (0) (0) (ii) Selected quotes from stakeholder focus group discussions and informal interviews ‘With the knowledge gained of the SDSS, I see it possible and essential to apply to other control provinces’ National Monitoring and Evaluation Officer, Solomon Islands ‘The SDSS has done a great job. It has made IRS operations planning easy and improves the households coverage (house sprayed)’ Temotu Province Field Officer, Solomon Islands ‘It helped me and my province a lot on how to plan out work in the field. Not like before.’ Isabel Province Monitoring Officer, Solomon Islands ‘Mapping is the direction for IRS planning and its operation. Mapping is to show location of houses sprayed and not sprayed. Mapping must be continued for any future program of the VBDCP. Mapping and IRS must be considered as “husband and wife” for good results’ Isabel Province IRS Spray officer, Solomon Islands ‘Useful on planning for any single activity (MBS, IRS, Bednet, Breeding Sites).’ Tafea Province Malaria Officer, Vanuatu ‘Continuous support needed at the National and Provincial VBDCP to sustain and upgrade upcoming information.’ Tafea Province Malaria Information System Officer, Vanuatu
Discussion
Experience from the SI and Vanuatu progressive elimination campaigns provide insight into how a SDSS has been applied in remote and challenging settings to address operational priorities of malaria elimination. Until recently, advanced GIS applications have been largely restricted to the domain of specialised experts, limiting the accessibility of such tools at a program implementation level [14].
A major focus in the development of the SDSS was to utilise GIS-based methods to equip malaria personnel to use programme data effectively to support strategic decision making and implementation throughout key phases of elimination, without a need to conduct complex or
99 time-consuming analysis. As detailed in Table 1A, specific SDSS applications have been developed to support all stages of priority intervention management (i.e. planning, implementation, monitoring and evaluation) as a means to enhance the delivery of essential services and achieve optimal coverage. The ability to identify active infection foci, highlight geographic areas requiring response, and automatically extract supplementary data to support the rapid deployment of response teams, including the preparation of budgets and allocation of required resources, can also be achieved within the SDSS-framework through automated
GIS-based queries. The SDSS also provides a historical and geospatial record of individual malaria cases, providing a mechanism to carry out epidemiological investigation and understand variation in transmission over time.
A lack of access to high-resolution data and digital mapping technology continue to remain major research challenges for contemporary malaria elimination [6, 15]. Modernised GR mapping techniques integrated into the SI and Vanuatu SDSS demonstrate how health programmes can efficiently collect large amounts of high-resolution field data, and readily access and utilise the acquired information via a digital map-based platform. Spatial clusters of poor service delivery can be quickly identified during the implementation of priority interventions via the SDSS map interface so that relevant follow-up operations can be effectively targeted (Figure 1A). Digital mapping technology is also used to conduct high- resolution geospatial surveillance-response (Figure 1B).
During the initial phases of SDSS application, training and technical support were provided
to provincial and national malaria SM&E personnel. Since inception of the SDSS framework
for elimination in 2008, additional applications are now being independently adapted to guide
national surveillance and intensified malaria control in both countries, emphasising the high
100 acceptability of this framework. As operational capacity has increased, technical support has shifted to focus on the provision of remotely-based mentorship to SM&E counterparts, predominantly via email and web-based teleconferencing. Additionally, a joint SI and
Vanuatu Working Group was established in 2012, with the intention to create an accessible network for SM&E personnel in both countries to collaborate, access support and continue to build operational capacity within the region.
In the context of malaria intervention management, the expert knowledge of local programme personnel is essential to ensure effective implementation [16]. A primary aim of the SDSS was to provide a feasible and accepted operational tool for implementation of elimination interventions and a key focus during development was to ensure the engagement of provincial malaria personnel. Placing an emphasis on transitioning from a traditional style of monitoring and evaluation to active surveillance-response, and ensuring that SM&E officers are the primary focal point for SDSS operation and management in their respective provinces is seen as an integral component to sustaining its effective implementation and assuring health systems integration.
An essential consideration of the SDSS framework is its dependence upon the performance of the broader health system. Key factors such as health worker performance, timely reporting, accurate diagnostics, the ability to implement an adequate response, and strong community engagement all influence the effectiveness of the framework. In the context of SI and
Vanuatu, the relatively small user base of provincial and national SM&E officers remains a limitation of the current the SDSS framework. A need exists to broaden the use and application of SDSS for malaria and other infectious disease management to further validate these systems. Additional efforts for the further validation of SDSS in the Pacific and other
101 settings are required to quantify and monitor the benefits of SDSS for elimination over the long term.
Information technology considerations such as access to user-friendly software, commercial
licensing and availability of reliable GIS data present limitations and challenges to any SDSS
framework. However, as access to web-based services and the availability of free, open-
source software and data continues to increase alongside a rapidly expanding global
telecommunication and portable digital device market (including smart phone and tablet
computers), opportunities to overcome information technology limitations that have long
been associated with resource-poor settings are improving.
Whilst the SDSS framework presented demonstrates how GIS-based decision systems can support progressive elimination, the adopted approaches have broader relevance for public health and infectious disease management. Significant scope exists to expand SDSS-based applications to support not only malaria elimination, but any facet of infectious disease management with a spatial or geographic context. Future steps forward will focus on the further validation and strengthening of SDSS applications for malaria elimination and intensified control, developing and broadening new SDSS-based operational tools for the control of other infectious diseases, and research to investigate new opportunities to utilise geospatial, telecommunication and web-based technologies for disease control in resource- poor countries.
102
Acknowledgements
We thank the support and collaboration of the Solomon Islands and Vanuatu National Vector
Borne Disease Control Programmes (NVBDCPs). In particular, we acknowledge the
continuous hard work and dedication of the provincial malaria officers and field staff from
the malaria elimination provinces of Isabel and Temotu, Solomon Islands and Tafea and
Torba, Vanuatu. Lasse S. Vestergaard is a staff member of the World Health Organization.
The author alone is responsible for the views expressed in this publication and they do not necessarily represent the decisions or policies of the World Health Organization.
Competing Interests: None declared.
103 References
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105
Chapter 7
Discussion
This Chapter summarises the findings of the studies presented in Chapters 2, 3, 4, 5, and 6.
Overall implications, limitations and future research directions extending from these findings are also discussed.
7.1 Key research findings
The body of research presented in this thesis outlines the development and implementation of unique geospatial tools designed to provide operational support for malaria programs
pursuing the goal of elimination in remote and resource-poor settings. The primary aims were to examine the role of a spatial decision support system (SDSS) framework in meeting the operational priorities of malaria elimination using the examples of Solomon Islands and
Vanuatu. Specific components addressed within the body of research have included: the application of geographical reconnaissance (GR) using modern geospatial tools to map, define and quantify target populations and household structures in the elimination provinces of Solomon Islands and Vanuatu; the development of an integrated SDSS to provide a framework for the coordination, implementation, monitoring and evaluation of priority interventions including focal Indoor Residual Spraying (IRS) and Long Lasting Insecticidal
Net (LLIN) distribution; a SDSS to support real-time passive and active malaria case detection, high-resolution surveillance-response and investigation; and evaluation of the application and effectiveness of a SDSS approach for malaria elimination.
106 Modern geographical reconnaissance data collection
Health system constraints, including a lack of information, communication and decision- support tools (particularly at the district and heath facility levels, which are critical for program coordination and implementation), continue to remain significant challenges for many endemic countries pursuing malaria elimination [1]. The ability for malaria programs to collect and access detailed and accurate data to strengthen decision-making and generate useful information, knowledge and evidence throughout all facets of program implementation is essential for malaria elimination [2-4]. As outlined in Chapters 2 and 3 of the thesis, the application of GR to generate information to support malaria intervention management is not
a new concept. These operations were widely conducted during the World Health
Organization’s (WHO) global malaria eradication period (GMEP) of the 1950s and 1960s to collect relevant data and generate information to support priority interventions such as IRS.
However, with the scaling-up of interventions as part of renewed global malaria elimination efforts, the role of GR, particularly in remote and resource-poor settings, has been limited and is often non-existent.
Whilst GR in its traditional form is effective, the manual and time-consuming methods remain significant barriers to the wide-scale application of these approaches in modern malaria elimination programs. In Chapter 3, personal digital assistants (PDA) with integrated global positioning systems (GPS) were used to conduct GR household mapping in the elimination provinces of Solomon Islands and Vanuatu. Prior to this research, the Solomon
Islands and Vanuatu malaria programs did not have the capacity to collect detailed baseline information in the target elimination provinces. Based on the household and demographic data collected during the GR, the spatial distribution of the elimination target areas were
107 defined and mapped, with these spatial data forming a basis for the development of customised district-based SDSS frameworks.
Digital data collection has been shown to be more accurate and cost-effective than traditional paper-based surveys [5-11]. These benefits, coupled with the increased mapping accuracy associated with GPS technology, indicate the advantages of modern GR over traditional
methods to generate the large-scale, accurate and detailed data required to support the
increased operational demands of malaria elimination. Findings from the GR studies conducted in this thesis are significant because they demonstrate the use of modern geospatial
tools to efficiently capture data that previously could not be effectively collected across large
geographic areas in remote settings. Household distribution maps and detailed data
summaries presented in Chapter 3 and in Appendices B and C illustrate the capacity of
modern GR to swiftly define target populations and generate relevant demographic and
household data within a SDSS framework to guide decision making and program
implementation for elimination.
Findings from Chapter 3 also provide an indication of the efficiency of modern GR methods.
As reflected by the results, a large number of households (10,459), people (43,497) and
household structures (30,663) were digitally recorded and mapped across a total land mass of
approximately 1103.2 km2, by relatively small field teams of between 4 to 12 individuals
using between 2 to 4 integrated PDA / GPS devices per team, over a total of 77 days. The non-specialist nature of the GR mapping and data collection field teams is also of significance with regards to the potential for malaria programs to successfully implement
modern GR. Prior to the initial operations conducted in 2008, all VBDCP personnel
108 participating in the fieldwork were inexperienced in modern GR methods, having only
attended short training sessions before the commencement of field activities.
Intervention management
An essential operational priority of elimination is the need for malaria programs to ensure the
effective delivery of priority interventions and services at optimal levels of coverage [1-3, 12,
13]. Innovative approaches towards information management, communication and decision support, particularly at the district coordination levels, are required to prevent health system bottlenecks impeding the effective delivery, monitoring and evaluation of required interventions and services [1]. Prior to the development of a SDSS framework in Solomon
Islands and Vanuatu, the respective malaria elimination provinces were limited by a largely paper-based information management system that lacked the precision required to effectively manage interventions and ensure their optimal delivery across entire target areas. Findings from this thesis are significant because they demonstrate how a SDSS has provided an effective tool for malaria programs to access and use detailed information, knowledge and evidence to strengthen decision making and support the coordination of priority interventions throughout key phases of malaria elimination.
In Chapter 4, a customised GIS-based SDSS framework was developed to support focal IRS operations on Tanna Island, Tafea Province as part of Vanuatu’s pre-elimination program phase. Results from this chapter demonstrate the role of a SDSS framework in supporting the effective coordination and implementation of a nominated priority intervention to ensure optimal coverage is achieved. Final coverage results (94.4% of households and 95.7% of the population) and the high user acceptability recorded during these studies indicate the overall
109 effectiveness of a SDSS for ensuring the delivery of essential interventions for malaria
elimination.
Specifically, during the planning phase of the IRS intervention on Tanna, the map-based interface enabled provincial malaria officers to identify and delineate a target zone for IRS based on previous assessments of the spatial distribution of malaria transmission. The identified zone was then broken down into manageable operational areas based on visual assessment of the spatial distribution of the population using the digital GR data and logistical considerations such as access and transport. Of significance during the planning phase was the ability for the Tafea Province program officers to interactively delineate operational area boundaries using the SDSS and automatically generate household and population data summaries for each individual area. These data were then used to develop operational timelines and to coordinate the allocation and deployment of required resources such as insecticide, equipment and personnel to respective areas, avoiding critical logistical bottlenecks that commonly affect service delivery in remote areas, such as insufficient resource allocation and stock-outs.
During IRS implementation, coverage by household was monitored visually using the SDSS map interface. As illustrated by Figure 6 in Chapter 4, households not sprayed during the first round of IRS could be highlighted by surveillance officers and maps produced to support follow-up operations as a means to ensure optimal coverage was achieved within the designated target areas. The SDSS also provided a tool to evaluate and report overall coverage following the completion of the intervention through the automatic generation of data summaries and household coverage maps.
110 An additional significant finding from the studies conducted in Chapter 4 was the ability for
malaria programs to swiftly analyse final IRS coverage results without the use of complex
statistical analysis to strategically guide additional follow-up activities and improve the
implementation of future interventions. Following completion of the initial IRS operation on
Tanna in 2009, Tafea Province Surveillance Officers were able to examine maps of
households that were not sprayed. Based on these investigations, several specific clusters of households dominated by a high proportion of refusals were located. This information was then used to highlight areas that required the targeting and development of appropriate behaviour change communication (BCC) interventions. Following increased awareness and
extensive consultation with community leaders in these areas, the VBDCP was able to successfully advocate the role and importance of community participation for malaria
elimination, and IRS coverage was significantly improved in these areas in the subsequent spray cycles.
Summary data and coverage maps presented in Appendices D and E also demonstrate how a
SDSS framework was used to achieve uniform household LLIN coverage in Temotu
Province between 2009 and 2010. Household LLIN distribution was coordinated using a
SDSS approach based on methods similar to those presented in Chapter 4 (which refers only to IRS). In 2009, the Temotu Province VBDCP experienced a stock-out of LLINs due to a shortage of supply at the national level. As reflected in the summary results and LLIN distribution maps, household coverage achieved in 2009 was low (53.1%). As a result of these distribution data being recorded in the provincial level SDSS, the Temotu malaria officers were equipped with a tool to accurately identify and locate households requiring
LLINs when additional stock was received in 2010 and operations were recommenced, ensuring that high uniform household coverage (96.1%) was achieved within the same
111 distribution cycle. Whilst this concept may appear straightforward, health system constraints
such as weaknesses in information management at critical coordination hubs at a district level, and a lack of decision support tools to cope with logistical bottlenecks such as stock- outs, remain significant operational challenges for many malaria endemic countries, often resulting in the poor quality and inequitable distribution of services and a reduction in the effectiveness of interventions [1].
Prior to the implementation of the SDSS in Temotu, the provincial malaria office did not have an information management system that was capable of recording and easily accessing detailed records on intervention distribution data. With the integration of intervention and service delivery data into the SDSS framework, malaria officers are equipped with a mechanism to store and access detailed historical records and evidence of activities conducted at household level as part of the elimination campaign. Information generated in the SDSS can then be used to guide the strategic and responsive planning and implementation of further interventions, as well as support epidemiological investigations. With interventions such as focal IRS and LLIN distribution, where the product has a finite lifespan, it is essential that detailed records are accurately kept and updated, particularly at the district level coordination hubs, to support strategic and effective planning of future follow-up or replacement cycles as required.
The SDSS-based approaches adopted in Chapter 4 and Appendices D and E have wide ranging significance beyond managing IRS and LLIN interventions for malaria elimination and intensified control alone. Approaches presented in the thesis could be modelled and replicated for any health intervention or service where efficacy is dependent upon ensuring that optimal spatial (and temporal) coverage is achieved within a target population.
112
High-resolution surveillance and targeted response
Surveillance is a core component of malaria elimination. The capacity to effectively detect and locate malaria infections, actively identify and classify transmission foci and target appropriate responses swiftly are essential priorities of malaria programs pursuing elimination [1-4, 14-16]. Most countries aiming for elimination do not yet have operational surveillance systems that can effectively meet these requirements [14]. Upon embarking on elimination, neither the Solomon Islands nor Vanuatu had the operational capacity or systems required to implement effective surveillance and response.
Good quality and up-to-date information acquired by routine surveillance integrated into existing health systems can provide a mechanism to effectively use minimal program data to conduct near real-time epidemiological assessment and support appropriate decision making
[17, 18]. Findings from Chapter 5 are significant because they demonstrate the application of an integrated SDSS-based framework designed to implement high resolution surveillance and guide targeted response for malaria elimination in Solomon Islands and Vanuatu. As illustrated in Figure 2 of Chapter 5, these studies have presented a unique district-level framework that links critical components of surveillance and response including passive and active case detection, individual case classification and epidemiological investigation, active transmission foci classification, and targeted responses.
Results from Chapter 5 illustrate how individual malaria cases can be located and mapped in a SDDS to view the spatial distribution of infections at a detailed household level within an elimination zone. As illustrated in Appendix H, the SDSS user interface also allows malaria surveillance officers to access individual case details as well conduct detailed searches of
113 collective cases via both an interactive map window and a standard query-based dialogue
box. Key elements of the query dialogue box include the ability to search cases by species
type; transmission mode (i.e. local or imported); date range; and to map case timelines. The
ability to access, query and visualise detailed case data provide an operational tool for
surveillance officers to conduct detailed epidemiological investigation, swiftly assess patterns of transmission and support decision making at a district level. For official certification of malaria elimination by the WHO, a country must have the capacity to demonstrate that there has been no evidence of local malaria transmission for a period of three years [3]. Because the SDSS-based surveillance response systems developed in Solomon Islands and Vanuatu serve as detailed historical records of individual cases and malaria transmission, it is anticipated that these platforms will provide a significant resource to support future elimination certification processes in these countries.
A key objective of public health surveillance is to provide information to guide interventions
[19]. In the context of malaria elimination, surveillance systems must provide malaria programs with a mechanism to rapidly detect and locate malaria infections and implement appropriate response measures to halt the occurrence of ongoing transmission and prevent the reintroduction of transmission in receptive areas resulting from imported cases. As malaria transmission declines, surveillance for elimination must become an active intervention itself to find and treat asymptomatic infections and seek out reservoirs of sustained transmission in both “hotspot” geographic areas and demographic “hot populations” of high risk individuals
[15, 20-24]. Active case detection (ACD) measures must effectively target high-risk geographic areas and populations to maximise impact and efficiency [23]. The studies conducted in Chapter 5 demonstrate how the spatiotemporal distribution of detected cases can be utilised within a SDSS framework to locate individual cases, classify and map active
114 transmission foci and highlight geographic areas of interest (AOI) to conduct targeted
reactive case detection and appropriate rapid response measures. As Figure 6 in Chapter 5
illustrates, geographic AOI regions can be automatically highlighted via a map interface.
Relevant operational information such as the locations of transmission foci and the number of
target households and people within the AOI can be generated and disseminated to support
the rapid mobilisation of response teams. Whilst not addressed directly during these studies,
scope also exists to utilise GR based data collection methods within a SDSS framework to
identify and locate “hot populations” and high-risk individuals such as mobile workers as a
means for targeting additional appropriate interventions relevant to a specific demographic.
A spatial decision support system framework for malaria elimination
As previously stated in the thesis, the effective and efficient management of intensified
malaria control and elimination has explicit geographic implications due to the spatial
heterogeneity and micro-epidemiological variability of malaria transmission [20, 21, 25-31].
In line with this evidence, there is call for further exploration, development and application of mapping tools to support the malaria elimination agenda [14, 15, 32]. Previous research has
also shown that map-based formats improve the accuracy and efficiency of complex
decisions for tasks where there is a geographical dimension, allowing for the rapid
interpretation and dissemination of spatial information [33-41]. The SDSS framework
developed in this thesis addresses the research call for the development of mapping tools for
malaria elimination, utilising a map-based approach to information management as a means
to support the implementation of priority interventions.
Studies in this thesis examine the development and implementation of an integrated SDSS to
support real-world malaria elimination programs in Solomon Islands and Vanuatu. A major
115 focus during the development of the SDSS framework was to provide an operational tool to strengthen district level coordination, management and decision making. Chapter 6 provides a retrospective analysis of the overall evolution and application of the SDSS framework in
Solomon Islands and Vanuatu since its inception in 2008. This chapter presents updated data on GR, LLIN and IRS intervention, and surveillance operations that have been supported by the malaria elimination SDDS framework. The integrated role of the SDSS framework and its dependence on the broader health system is also described.
Findings from Chapter 6 reflects the ongoing capacity and growth of the Solomon Islands and
Vanuatu National Malaria Programs (NMPs) to continue to implement modern GR for elimination following the initial GR operations outlined in Chapter 3. Results from this chapter show that, as of 2013, 20,733 households, 55,711 building structures and a population of 91,319 have been captured through modern GR. These data have been used to update the
SDSS to define the target populations in all four of the nominated malaria elimination
provinces. Additionally, results from this chapter have shown that LLIN and IRS
interventions have maintained and reported high levels of household coverage (97.5% and
91.7% respectively) through a SDSS-based management approach; and in Isabel Province,
Solomon Islands, where high-resolution surveillance for elimination has been intensified, a
high proportion (95.4%) of malaria cases continue to be successfully identified and mapped
at a household level to guide the SDSS surveillance-response framework.
Chapter 6 highlights the high acceptability of the SDSS framework amongst end users and
reflects upon key elements in regards to the technical development of the SDSS and the
ongoing operational capacity of the framework in Solomon Islands and Vanuatu. These
include the importance of engaging directly with the district-level elimination program
116 personnel to ensure the appropriate design and development of a SDSS specific to local settings; and ensuring the district-level surveillance, monitoring and evaluation officers were the primary focal point for the operation and a management of the SDSS in their respective elimination provinces. Additionally, essential to sustaining the operational capacity of the
SDSS frameworks in these remote settings is providing a mechanism for personnel to access continuous technical support and mentorship as needed. This Chapter highlights the importance of developing and maintaining ongoing working relationships between in-country and inter-regional program counterparts, as well as external technical partners, highlighting opportunities associated with the increasing availability of communication resources such email and web-based teleconferencing at the district level in Solomon Islands and Vanuatu.
7.2 Limitations and constraints of the study
The relatively small user base of SDSS operators in Solomon Islands and Vanuatu precluded
the conduct of meaningful statistical analysis on user acceptability of the SDSS framework.
Whilst the continued application and adoption of the SDSS in Solomon Islands and Vanuatu
indicates high acceptability, there is a need to expand the user base and application of SDSS
for further validation and quantification of user acceptability. As SDSS-based applications
are currently expanding in Solomon Islands and Vanuatu to support intensified control and
the management of additional infectious diseases, and are rolling out in additional countries including Swaziland [42] and Bhutan, it is anticipated that further robust statistical analysis on user acceptability will be possible in the future.
Currently there are no formalised indicators or monitoring frameworks to quantify the impact of a SDSS on malaria elimination [43]. Table 1A in Chapter 6 outlines key markers and
117 determinants that have been used in this thesis as a means to assess the performance of
individual SDSS-based applications in Solomon Islands and Vanuatu relative to specific
operational priorities of elimination. Currently, these indicators do not include an assessment of cost effectiveness. Additionally, as both Solomon Islands and Vanuatu are still in the early stages of elimination, it was not possible to address the long-term impacts of the SDSS in this
thesis. As SDSS applications continue to support elimination efforts in Solomon Islands and
Vanuatu and are expanded elsewhere, there is a need for the continued development, standardisation and validation of a quantifiable monitoring and evaluation framework for assessing the performance of the SDSS that builds upon the indicators developed as part of this thesis, and incorporates both cost and the long term impacts for malaria elimination.
An additional constraint of the SDSS framework developed to support malaria elimination in
Solomon Islands and Vanuatu is the dependence upon the collection of baseline GR household data as the platform for the SDSS. Whilst GR provides an ideal opportunity for malaria programs to collect detailed program specific data, in some countries or specific geographic areas it may not be operationally feasible to conduct independent GR operations prior to the implementation of malaria elimination interventions. Population surveys that include information on location, such as those conducted by government departments (e.g. statistics, land and environment), non-government organisations or donor agencies; as well as
the growing availability of free web-based high-resolution remotely sensed data and satellite
imagery [42-47] also provide potential alternative sources of baseline information that can be
utilised within a SDSS.
The SDSS framework presented in this thesis was developed around a commercial GIS
software platform. As SDSS are computer-based systems, an investment in both computer
118 hardware and software is required. Information technology considerations such as access to
user-friendly software, commercial licensing and maintenance present some challenges to the
establishment of SDSS frameworks in remote settings. However, opportunities to overcome
information technology limitations that have long been associated with resource-poor settings are improving with expanding global internet coverage, a rapidly growing telecommunications and portable digital device market (including smart phone and tablet computers), increased access to web-based services and the availability of free open source software and data.
Throughout the course of the studies in this thesis, there were limited baseline entomological
data available in the elimination provinces of Solomon Islands and Vanuatu. This limited the
potential to integrate and utilise these data within the SDSS framework. In the context of the
automated GIS query application developed to classify transmission foci as part of the
surveillance-response system presented in Chapter 6, geographic areas of assumed receptivity
were used in relation to the spatiotemporal distribution of detected cases. Because the method
adopted was dependent upon the capacity of the health system to accurately detect, classify
and report malaria cases, the integration of updated entomological survey data into the SDDS
framework would add additional strength to the surveillance-response application presented.
7.3 Future research directions and steps forward
SDSS-based operational tools for malaria elimination, intensified control and infectious
disease management remain in relative infancy. Key findings and lessons learned from the
studies presented in this thesis provide significant opportunities for further research. This
should focus on the application of SDSS approaches to support infectious disease and public
119 health management, particularly in the context of health systems strengthening and the development of practical operational tools to support strategic decision making and implementation in resource poor and remote settings. Future research directions and steps forward should include:
• Expanding upon the studies conducted in the thesis to determine essential key
indicators for measuring the operational impacts of SDSS tools and develop a
formalised framework using both quantitative and qualitative research methods for
evaluating the performance, benefits and cost-effectiveness of a SDSS-based
approach for malaria elimination.
• Implementing additional prospective studies on the role and implementation of SDSS-
based tools for malaria elimination to aid the assessment of specific operational
impacts for malaria programs across differing epidemiological settings.
• Expanding the scope of SDSS-based applications to support the surveillance and
stratification of malaria for intensified control.
• Expanding the scope of SDSS-based applications to support the operational
management of additional infectious diseases that are considered to have significant
geospatial heterogeneity such as dengue, lymphatic filariasis, schistosomiasis and
sleeping sickness.
• Continuing to investigate opportunities for developing operational tools and
strengthening health systems in remote and resource-poor settings using geospatial,
120 high-resolution digital data collection, telecommunication and web-based
technologies.
7.4 Conclusions
The research presented in this thesis outlines the development, application and evaluation of a
unique SDSS framework designed to address the scaled-up demands of malaria elimination in
the Pacific Island countries of Solomon Islands and Vanuatu. This research has combined
knowledge and lessons learnt from past efforts to eradicate malaria, contemporary global
elimination strategies and research priorities, and modern advancements in geospatial and
information technology to develop an integrated operational tool to support malaria programs
throughout the transition from programs focussing on intensified control, pre-elimination,
elimination and the eventual prevention of reintroduction. Operational research focus was
particularly directed at strengthening capacity at the critical district and health facility
coordination levels to maximise the use of minimal essential data to support strategic
decision making and targeted implementation through geospatial technology.
These studies have demonstrated how a SDSS-based framework can provide program managers with an operational tool to address key priorities of malaria elimination including the need to ensure the effective delivery of scaled-up services and interventions at optimal levels of coverage in target areas; the ability to implement detailed surveillance to actively identify transmission foci and target appropriate responses swiftly; and provide detailed, accurate data to facilitate decision-making and generate useful information, knowledge and evidence throughout all facets of program implementation.
121 The studies conducted in this thesis have important public health implications because they present a shift away from conventional specialist forms of exploratory geospatial research towards a focus on the development and application of operational geospatial tools to support real-world malaria elimination programs. Findings from these studies have additional applicability beyond malaria elimination. The SDSS-based applications developed and the approaches adopted have broad relevance for management of a wide range of infectious diseases, where decisions have a spatial or geographic component, and should be explored further.
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146 Appendix A: An overview of WHO recommended interventions by programme type to support the transition from malaria control to elimination (source WHO: Malaria elimination - A field manual for low and moderate endemic countries. Geneva: World Health Organization;
2007).
PRE-ELIMINATION PREVENTION OF INTERVENTION CONTROL PROGRAMME ELIMINATION PROGRAMME PROGRAMME1 REINTRODUCTION PROGRAMME - Update drug policy, use of ACT - Drug policy change to: - Implementation of new drug policy - Case management of imported malaria - QA/QC of laboratory diagnosis radical treatment for P. vivax; ACT - Routine QA/QC expert microscopy - Awareness of drug resistance patterns (microscopy/RDT) and gametocyte treatment for P. - Active case detection abroad, to formulate prevention guidelines - Clinical diagnosis sometimes falciparum - Monitoring antimalarial drug Case management acceptable - 100% case confirmation by resistance - Monitoring antimalarial drug microscopy resistance - Microscopy QA/QC - Monitoring antimalarial drug resistance - Transmission reduction through high - Geographical reconnaissance - Geographical reconnaissance - Perfect malaria case detection population coverage of ITN/LLIN and - Total IRS coverage in foci - Vector control to reduce transmission mechanism IRS - IVM and ITN/LLIN as in residual active and new active foci - Cluster response and prevention Vector control and - Entomological surveillance complementary measures in specific - Vector control to reduce receptivity in - Prevention of malaria in travellers, malaria - Epidemic preparedness and response situations recent foci including health education and prevention - IPTp in hyperendemic areas - Epidemic preparedness and - Outbreak preparedness and response engagement of travel agencies response - Entomological surveillance - Entomological surveillance - Prevention of malaria in travellers - Improve surveillance and national - GIS-based database on cases and - Case investigation and classification - Vigilance coverage vectors - Foci investigation and classification - Case investigation - Country profiles - Elimination database - Routine genotyping - P. falciparum outbreak notification in Monitoring and - Malaria indicator surveys (MIS, - Central records bank - Malaria surveys accordance with IHR (2005) evaluation MICS, DHS) - Genotyping, isolate bank - Immediate notification of cases - Annual reporting to WHO on - Malaria surveys - Meteorological monitoring maintenance of malaria-free status - Immediate notification of cases - Access to treatment - Engaging private sector - Full cooperation of private sector - Integration of malaria programme staff - Access to diagnostics - Control of OTC sale of antimalarial - No OTC sale of antimalarial into other health and vector control Health system - Health system strengthening medicines medicines programmes issues (coverage, private and public - Availability of qualified staff - Free-of-charge diagnosis and sectors, QA) treatment for all malaria cases
147 Appendix A Continued
PRE-ELIMINATION PREVENTION OF INTERVENTION CONTROL PROGRAMME ELIMINATION PROGRAMME PROGRAMME1 REINTRODUCTION PROGRAMME - Procurement, supply management - Elimination programme - Implementation of elimination - WHO certification process - Resource mobilization development programme - Regional initiative - Legislation - Implementation of updated drug - Pharmacovigilance - Regional initiative policy, vector control, active detection Programmatic - Adherence to the “Three Ones” - Mobilization of domestic funding of cases issues principles - Establish malaria elimination - Malaria elimination committee: - Integration with other health committee manage malaria elimination database; programmes for delivery of - Reorientation of health facility staff repository of information; periodic interventions, e.g. ITN/LLIN, IPTp review; oversight - Domestic/external funding - Reorientation of health facility staff - Case management - Integrated vector management, including monitoring of insecticide resistance - Geographical information collection - Human resources development Interventions - Health education, public relations, advocacy throughout all - Operational research programmes - Technical and operational coordination, including intra- and intersectoral collaboration, both within the country and with neighbouring countries - Monitoring and evaluation - Independent assessment of reaching milestones - Resource mobilization - Health systems strengthening ACT: artemisinin-based combination therapy; DHS: Demographic and Health Surveys; GIS: geographic information system; IHR (2005): International Health Regulations (2005); IPTp: intermittent preventive treatment in pregnancy; IRS: indoor residual spraying; ITN: insecticide-treated mosquito net; IVM: integrated vector management; LLIN: long-lasting insecticidal net; MICS: Multiple Indicator Cluster Surveys; MIS: Malaria Indicator Survey; OTC: over-the-counter; QA: quality assurance; QC: quality control; RDT: rapid diagnostic test. 1The pre-elimination programme is a reorientation phase. The interventions mentioned in this column are introduced during this programme reorientation, to be fully operational at the start of the elimination programme.
148 Appendix B: Additional File Material published as part of the peer-reviewed article forming Chapter 3: Kelly GC, Hii J, Batarii W, Donald W,
Hale E, Nausien J, Pontifex S, Vallely A, Tanner M, Clements A: Modern geographical reconnaissance of target populations in malaria elimination zones. Malar J 2010, 9:289.
Additional file 1: Demographic description of the population of Solomon Islands and Vanuatu Geographical Reconnaissance operation areas, 2009. Data provides a detailed demographic description by age and gender of the population recorded within each geographical reconnaissance operation area
< 1yrs 1-4yrs 5-15yrs >15yrs GR Operation Operation Zone Male Female Male Female Male Female Male Female Total
Duff Islands 4 7 35 31 83 48 175 173 556 Reef Islands 84 65 361 372 890 854 1534 1798 5958 GR1:Outer Islands, Temotu Province Utupua 15 16 85 82 213 190 355 344 1300 Vanikolo 23 17 94 90 248 233 438 459 1602 GR1: Total 126 105 575 575 1434 1325 2502 2774 9416 Santa Cruz East 36 37 213 215 659 517 1109 1164 3950 GR2: Santa Cruz, Temotu Province Santa Cruz West 77 87 402 380 1166 1045 2394 2611 8162 GR2: Total 113 124 615 595 1825 1562 3503 3775 12112 Health Zone 1 19 14 307 301 1068 1070 1244 1340 5363 Health Zone 2 93 97 719 649 1780 1669 2115 2273 9395 GR3: Tanna IRS Zone, Tafea Province Health Zone 3 17 17 178 186 638 584 685 791 3096 Health Zone 4 34 34 277 248 871 772 895 984 4115 GR3: Total 163 162 1481 1384 4357 4095 4939 5388 21969
149 Appendix C: Additional File Material published as part of the peer-reviewed article forming Chapter 3: Kelly GC, Hii J, Batarii W, Donald W,
Hale E, Nausien J, Pontifex S, Vallely A, Tanner M, Clements A: Modern geographical reconnaissance of target populations in malaria elimination zones. Malar J 2010, 9:289.
Additional file 2: Household structures summary table of Solomon Islands and Vanuatu Geographical Reconnaissance operation areas, 2008-2009. Data provides a detailed breakdown of the number of structures recorded by type within each individual geographical reconnaissance operation area.
GR Rest Storage Garden Nakamal Other Operation Zone Houses Kitchens Toilets Lodges Dryers Total Operation Shelters Sheds Houses Houses Structures
Duff Islands 196 123 NR 24 3 22 NR 6 NR 25 399 GR1:Outer Reef Islands 1551 1160 NR 149 107 221 NR 154 NR 404 3746 Islands, Temotu Utupua 339 234 NR 34 26 56 NR 2 NR 79 770 Province Vanikolo 422 302 NR 17 30 84 NR 1 NR 82 938 GR1: Total 2508 1819 NR 224 166 383 NR 163 NR 590 5853 GR2: Santa Santa Cruz East 855 615 NR 71 50 80 NR 98 NR 104 1873 Cruz, Temotu Santa Cruz West 1948 1319 NR 97 96 204 NR 87 NR 295 4046 Province GR2: Total 2803 1934 NR 168 146 284 NR 185 NR 399 5919 Health Zone 1 1778 1001 988 NR 376 NR 1 NR 120 950 5214 GR3: Tanna Health Zone 2 3123 1654 1345 NR 392 NR 26 NR 134 625 7299 IRS Zone, Health Zone 3 1202 593 404 NR 140 NR 0 NR 51 533 2923 Tafea Province Health Zone 4 1310 806 670 NR 169 NR 6 NR 61 433 3455 GR3: Total 7413 4054 3407 NR 1077 NR 33 NR 366 2541 18891
*NR: Structure type not individually classified during census operations following initial stakeholder collaboration and structure surveys within each target area.
150 Appendix D: Long lasting insecticidal net (LLIN) distribution SDSS household coverage data generated in the Temotu Province SDSS, 2000 - 2010
Total Recorded LLIN Coverage 2009 LLIN Coverage 2010 Island Populated Populated Populated Group Population Population Population Households* Households* Households* 100% 100% 100% 100% Duff Islands 118 556 (118) (556) (118) (556) 67.8% 69.2% 95.6% 96.8% Reef Islands 1179 5970 (799) (4128) (1127) (5779) 100% 100% 100% 100% Utupua 259 1293 (259) (1293) (259) (1293) 79.2% 77.6% 98.7% 99.1% Vanikolo 308 1602 (244) (1243) (304) (1588) 34.3% 34.7% 95.4% 95.6% Santa Cruz 2298 12169 (788) (4219) (2193) (11635) 53.1% 53.0% 96.1% 96.6% Total 4162 21590 (2208) (11439) (4001) (20851) * Only populated households are included in these data summaries i.e. non-populated and empty households and other structures were not included.
151 Appendix E: Long lasting insecticidal net (LLIN) distribution SDSS household coverage maps generated using the Temotu Province SDSS,
2009 – 2010
Duff Islands, Temotu Province SDSS generated LLIN household distribution coverage maps 2009 & 2010
152
Appendix E Continued
Reef Islands, Temotu Province SDSS generated LLIN household distribution coverage maps 2009 & 2010
153 Appendix E Continued
Utupua Island, Temotu Province SDSS generated LLIN household distribution coverage maps 2009 & 2010
154 Appendix E Continued
Vanikolo Island Group, Temotu Province SDSS generated LLIN household distribution coverage maps 2009 & 2010
155 Appendix E Continued
Santa Cruz Island Group, Temotu Province SDSS generated LLIN household distribution coverage maps 2009 & 2010
156 Appendix F: Additional File Material published as part of the peer-reviewed article forming Chapter 5: Kelly G, Hale E, Donald W, Batarii W,
Bugoro H, Nausien J, Smale J, Palmer K, Bobogare A, Taleo G, Vallely A, Tanner M, Vestergaard LS, Clements AC: A high-resolution geospatial surveillance-response system for malaria elimination in Solomon Islands and Vanuatu. Malaria Journal 2013, 12:108.
Additional file 1: Screenshot of the Isabel Province surveillance-response spatial decision support system user interface.
157 Appendix G: Additional File Material published as part of the peer-reviewed article forming
Chapter 5: Kelly G, Hale E, Donald W, Batarii W, Bugoro H, Nausien J, Smale J, Palmer K,
Bobogare A, Taleo G, Vallely A, Tanner M, Vestergaard LS, Clements AC: A high- resolution geospatial surveillance-response system for malaria elimination in Solomon
Islands and Vanuatu. Malaria Journal 2013, 12:108.
Additional file 2: Screenshot of the Temotu Province rapid case surveillance reporting digital data entry form template.
158 Appendix H: Screenshot of the Isabel Province surveillance-response spatial decision support system user interface illustrating the map-based and query-dialogue tools to support epidemiological investigation.
159