The application of geographical information systems to important public health problems in Africa
© Tanser and le Sueur; licensee BioMed Central Ltd. 2002
Received: 8 November 2002
Accepted: 9 December 2002
Published: 9 December 2002
Africa is generally held to be in crisis, and the quality of life for the majority of the continent's inhabitants has been declining in both relative and absolute terms. In addition, the majority of the world's disease burden is realised in Africa. Geographical information systems (GIS) technology, therefore, is a tool of great inherent potential for health research and management in Africa. The spatial modelling capacity offered by GIS is directly applicable to understanding the spatial variation of disease, and its relationship to environmental factors and the health care system. Whilst there have been numerous critiques of the application of GIS technology to developed world health problems it has been less clear whether the technology is both applicable and sustainable in an African setting. If the potential for GIS to contribute to health research and planning in Africa is to be properly evaluated then the technology must be applicable to the most pressing health problems in the continent. We briefly outline the work undertaken in HIV, malaria and tuberculosis (diseases of significant public health impact and contrasting modes of transmission), outline GIS trends relevant to Africa and describe some of the obstacles to the sustainable implementation of GIS. We discuss types of viable GIS applications and conclude with a discussion of the types of African health problems of particular relevance to the application of GIS.
The physical and ecological structure of Africa is as varied as its social, political and demographic characteristics . Major biomes in the continent include tropical rainforest, montane forest, moist and dry savanna, semi-desert and desert and temperate grasslands . The political environment, poverty and generally low levels of well-being for the majority of the people in the continent combine with the varied climatic conditions, vegetation and biogeography to explain the prevalence of disease-causing organisms, or pathogens such as bacteria, viruses and worms .
The applications of geographical information systems (GIS) to health and epidemiology have been critiqued by numerous authors [4–13] and although found to be under-utilised it has been concluded that GIS has much to contribute to the health sciences. However, it has been less clear whether GIS technology is both applicable and sustainable in an African setting. GIS is a tool of great inherent potential for health in Africa as health is largely determined by environmental factors (including the sociocultural and physical environment) which vary greatly in space. The spatial modelling capacity offered by GIS is directly applicable to understanding the spatial variation of disease, and its relationship to environmental factors and the health care system . Public health practice needs timely information on the course of disease and other health events to implement appropriate actions and GIS are an innovative technology for generating this type of information. Unfortunately, the importance of the spatial distribution of disease has been too often overlooked .
Africa is generally held to be in crisis and the quality of life for the majority of the continent's inhabitants has been declining in both relative and absolute terms. The health problems are different to those in the developed world and if GIS is to be used for the health challenges facing Africa, then it must respond to these realities and priorities. Due to infrastructural and cost constraints, there is a lack of reliable statistics and disease reporting in Africa. Where data do exist, they tend to be clinically (as opposed to diagnostically) based. Disease estimates in Africa can therefore range between 'educated guesses and wild speculation' . GIS can help significantly in this area by filling the gaps through empirical disease modelling techniques.
If the potential for GIS to contribute to health research and planning in Africa is to be properly evaluated then the technology must be applicable to the most pressing health problems in the continent. In this article we focus on the human immunodeficiency virus (HIV), malaria and tuberculosis as some of the most important public health threats in Africa [17, 18]as well as having diverse modes of transmission. Furthermore we review work done in the spatial analysis of health systems (that must assist in the attenuation and control of these diseases).
Africa's health priorities
HIV/AIDS is the leading cause of mortality and morbidity in Africa . Since its appearance more than two decades ago the virus has spread to almost every country in the world affecting an estimated 34 million people . Nearly 24 million people in Africa currently live with HIV/AIDS and the epidemic continues to ravage the development prospects for millions of Africans throughout the continent. In 1999, about 3.8 million Africans were infected with HIV during that year, and a total of 10.7 million children were estimated to be orphaned by it. The 21 countries with the highest HIV prevalence are in Africa. In South Africa, Botswana and Zimbabwe, one in four adults is infected. A child born in Zambia or Zimbabwe today is more likely than not to die of AIDS. In many other African countries, the lifetime risk of dying of AIDS is greater than one in three . While prevalence in many west and central African countries has remained relatively low and stable, eastern and southern Africa have experienced explosive epidemics with HIV prevalence exceeding 40% among pregnant women in some regions. Around 5 million new infections are currently occurring annually worldwide, over 90% in developing countries.
One of the reasons for the severity of Africa's HIV/AIDS epidemic is the high prevalence of other sexually transmitted infections (STIs) and the inadequacy of STI services. Another reason for the recent rise in HIV in Africa is the gradual adaptation to new environments, for example, as people migrate from rural to urban areas in search of work. However, the spread of sexually transmitted diseases can also be sharply intensified by crises such as natural disasters, social disintegration, armed conflict and mass population movements. HIV is especially burdensome as the infection and resultant disease primarily affects young and mature adults in their most productive years (15–25) when older and younger family members are dependent on them. The global HIV pandemic is composed of a series of several smaller epidemics. Even within Africa, where levels of infection are the highest in the world, there is substantial heterogeneity of levels of infection.
Tuberculosis is the leading infectious cause of death worldwide, killing more people aged over 5 years of age than AIDS, malaria, diarrhoea and all other tropical diseases combined. The World Bank estimate that the disease accounts for 26% of all avoidable adult deaths in less-developed countries. So serious is the threat of tuberculosis that in 1993, the World Health Organisation took the unprecedented step of declaring this disease a global emergency. HIV infection renders a person infected by Mycobacterium tuberculosis much more likely to develop overt tuberculosis, and the evolution of the disease is considerably accelerated. About 20% of tuberculosis cases in Africa are believed to be related to HIV infection. WHO has calculated that, unless urgent action is taken the annual global number of deaths could rise from 3 million to 4 million by the year 2004. The need for effective intervention is compelling because tuberculosis treatment is one of the most cost-effective of all health interventions. In response to this re-emerging epidemic, the World Health Organisation is promoting the DOTS control strategy (directly observed therapy, short course) with community based treatment at its core.
In the last decade, in Africa, the incidence of malaria has been escalating at an alarming rate. Cases in Africa account for 90% of malaria cases in the world . Until recently, malaria was ranked as the leading disease in terms of disease burden. It is now estimated that only HIV has a larger impact on the health of the African population than that of malaria. Malaria is estimated to cause disease in 400 million individuals in Africa and is responsible for 20–50% of all hospital admissions. Mortality associated with cerebral malaria has not improved in the past 30 years and severe malaria anaemia is on the increase. One study has estimated (using empirical methods) that during 1995, 0.75 to 1.3 million deaths resulted from malaria in Africa and that approximately 80% of these occurred in children < 5 years of age.
The development of drug-resistant strains of the malaria parasite Plasmodium falciparum has been one of the greatest obstacles to controlling the disease . Drugs such as chloroquine, which were once highly effective, are now almost useless for treating malaria in many parts of the world . Frequent armed conflicts, migration of non-immune populations, changing climatic patterns, adverse socioeconomic patterns (e.g. gross inadequacies of funds for drugs), high birth rates and changes in the behaviour of the vectors are also responsible for the upsurge. The upsurge has also been attributed in part to the declining nutritional status of individuals in both urban and rural areas. Malaria and underdevelopment are closely intertwined. The disease causes widespread premature death and suffering, imposes financial hardship on poor households, and holds back economic growth and improvements in living standards. Malaria flourishes in situations of social and environmental crisis, weak health systems and disadvantaged communities.
Health systems in Africa face increasingly diverse and complex health problems, rapidly growing populations, and severe resource constraints. Improving the performance of health systems has been identified as a major global health priority. Health systems' performance makes a profound difference to the quality, as well as the length of the lives of the billions of people they serve. If health systems are poorly constituted and managed, life-enhancing interventions cannot be delivered effectively to those in need. Malaria and tuberculosis are examples of diseases that thrive in the absence of well constituted, effective health systems. This is particularly pertinent for Africa where health systems often perform poorly and are unreliable.
GIS research in health in Africa
Much remains to be understood about the relationship between space and disease. The spatial dynamics of tuberculosis, HIV and malaria are different because of the different modes of transmission and differing relationships to the environment. For example, tuberculosis (transmitted by respiratory droplets) and HIV (transmitted largely through sexual contact) rely on close human contact for transmission. Malaria however is transmitted by mosquito and is constrained only by the flight distance of mosquitoes. This has been measured in one vector species at a maximum distance of 1.8 km . Climatic factors play a large part in determining the distribution of malaria, whereas HIV and tuberculosis are affected more by the social environment. These differences will necessarily affect the types of GIS methodologies used to understand the various spatial components of these diseases.
GIS has been widely applied to the understanding and management of malaria in Africa. For example GIS has been used to generate models of malaria occurrence [32, 33], seasonality [34, 35] and transmission intensity [36–41] using climatic and remotely sensed data. The outputs of such models have been combined with population data [16, 42] to estimate population exposure, mortality and morbidity [16, 42] and to analyse  and project [44, 34] the effects of climate change on malaria. GIS has been used to map malaria vectors [45–47], vector habitats  and infection . It has also been used in the management and control of malaria [50–52], to measure the effects of access to malaria treatment  and to evaluate the effects of intervention strategies . The above studies were undertaken at scales ranging from micro to continental.
We were only able to locate a handful of published studies using GIS to study tuberculosis in Africa, all of which were undertaken in South Africa. GIS has been used to map tuberculosis cases in an Urban area in the Western Cape of South Africa  and analyse childhood tuberculosis in two urban communities of Cape Town . In a rural area of KwaZulu-Natal, GIS has been used to analyse the distribution of treatment points and the effect of community-based (as opposed to facility-based) treatment on increased access to nearest treatment supervision point [56, 57].
GIS could have an important role to play in tuberculosis control programme management, service development, and research. In terms of planning and managing the service, GIS can assist in the planning of the number and distribution of the supervision points as proximity of treatment is one important factor in promoting adherence to treatment. Much remains to be understood about tuberculosis transmission dynamics in developing countries and GIS will be a useful addition to molecular techniques and conventional epidemiology, in elucidating transmission pathways, and clusters of multi-drug resistant cases for example.
Although several studies analysing geographic variations in HIV in Africa have been conducted [59–63], and the importance of place in targeting areas for priority intervention has been emphasised, only one published study could be located that applied GIS to the analysis of HIV . The study provided some evidence for an ecological relationship between transport accessibility (distance to roads) and HIV prevalence. This was believed to be related to the amount of sex work taking place along the major routes as well as the higher mobility of persons living near transport routes. However, this relationship needs to be tested at an individual as well as at an ecological level. Though studies have documented heterogeneity in the geographical distribution of the HI virus [66–68], much remains to be learnt about the causes and nature of this heterogeneity. Most research has focussed on temporal analysis ignoring the spatial dimensions of the HIV/AIDS epidemic. Yet, spatial analysis may be an important tool to monitor the epidemic, predict future treatment demands and to target areas for public health interventions. Work in Europe and North America [e.g. [69, 70]] has focused largely on the distribution and diffusion of the disease . In addition, the delimitation of high-risk areas (based on the distribution of co-factors) using standard risk analysis techniques could prove invaluable in Africa. Furthermore, the technology could also assist in the optimal spatial organisation of health care delivery including home-based care. The difficulty of obtaining HIV data and the stigma associated with disclosure however, is a major obstacle to the use of GIS in HIV research in Africa.
Surprisingly, there were few published examples of the use of GIS in health systems research in Africa but there is an encouraging amount of work in progress. One group used GIS to study inequalities in population per bed ratios and the implications of open access to the private and the formerly white hospital services in the province of KwaZulu-Natal, South Africa . One study used GIS to equitably distribute fieldworker workload in a large health survey. The methodology predicted average inter-homestead walking time and divided the heterogeneous study area into units of equal workload . The author suggests that an extension of the same methodology can be used to optimally distribute community health workers and tuberculosis DOT supervisors, for example. Another study analysed modal patterns of fixed and mobile clinic attendance across an integrated rural health district  and developed indices to analyse the relative attraction and repulsion by the various clinics in the district. The most important outcome of the research was the development of a composite measure of clinic usage and inter-clinic interaction based on the ratio of total actual versus predicted distance travelled to attend clinic. The same data set has been used to validate a model of travel time to the various clinics based on a network analysis of a road network. Relative clinic attraction and inter-clinic interaction were again studied using travel time as the denominator in the index (F.C. Tanser & K. Herbst, In prep., 2002). A study using a similar methodology is underway in the Rufiji district of Tanzania to investigate the relationship of wealth quintiles and health outcomes to travel time to nearest health facility (D. de Savigny et al., In prep., 2002). Researchers are using GIS to investigate the relationship between clinic access and maternal and child health indicators in rural Kwa-Zulu Natal South Africa (J. Tsoka et al., In prep., 2002) In four districts of Kenya, GIS is being used to capture and model both population's access and utilisation of health services with a view to increasing the effectiveness of malaria treatment coverage (A. Noor et al., In prep., 2002).
Limited physical access to primary health care is a major factor contributing to the poor health of populations in developing countries. The world health report of 2000  was dedicated to improving the performance of health systems. Health systems performance make a profound difference to the quality, as well as the length of the lives of the billions of people they serve. However, an important omission from the report was the spatial aspect of health systems research. GIS can be used to effectively spatially analyse health systems coverage and identify deficiencies. The potential exists for GIS to play a key role in rational and more cost-effective health service planning and resource allocation in Africa.
GIS trends relevant to Africa
GIS is largely technologically (as opposed to research) driven. Some of these global technological trends are irrelevant to health research in Africa at the present time. However, some global trends (both technological and non-technological) are of significant relevance to Africa's health crisis.
It is becoming clear that although GIS started out as a technological tool, it is rapidly evolving into a science in its own right, albeit in embryonic form. At present it lies somewhere along the continuum between the two. As software becomes increasingly powerful and new datasets become available and GIS is increasingly used to understand and forecast the dynamics of (particularly environmental) disease, this evolution is likely to continue. A parallel exists between GIS and epidemiology. In the same way that epidemiology evolved into a science in its own right in the 1970s , GIS is beginning to be recognised as a science. Like epidemiology its tenets have been established piecemeal  with contributions coming from a number of different disciplines, in particular the earth sciences. It is now time to draw the different facets of GIS together under the umbrella of geographic information science.
Computer hardware is becoming increasingly cheaper and more powerful, so that even complex analyses of GIS and image data can be carried out on a desktop computer. At the same time, commercial software has been developed into stand-alone solutions capable of performing increasingly complex tasks through increasingly user-friendly interfaces. Whilst there is an increasing amount of free software, the commercially available comprehensive packages remain expensive .
Since the 1st May 2000 the accuracy of off-the-shelf global positioning systems (GPS) has improved by an order of magnitude. Low cost units can now perform tasks that they previously weren't suitable for. This development is likely to result in a sharp increase in the number of geo-referenced health projects making use of GPS technology in the near future.
Obstacles to the advancement of GIS in health in Africa
The paucity of qualified staff, which has prevented many GIS projects from surviving the donor involvement phase, is a major problem in Africa . GIS applications in Africa are often found to be initiatives funded or supported by international aid agencies and many are pilot or research projects as opposed to operational systems. They also tend to be controlled by outsiders, not by African scientists. If GIS are to be useful and effective, then they must be introduced by local scientists who understand both the technological and the socio-economic context in which the systems are to operate. Training creates capacity and leads to an increase in terms of data needs. It however also provides the capacity to fulfil these needs and the new products that result are often of value to many other sectors. Capacity development of African staff should therefore be prioritised.
In addition to lack of capacity, a lack of suitable GIS data sets is a major impediment to the growth of GIS in Africa. The access to spatial data (which are fundamental to any GIS application) continues to be difficult and expensive. This is not specific to health but to all sectors that utilise GIS. There are similarities in the field requirements for using GIS between forestry, ecology, archaeology and epidemiology that could provide substantial benefits by the sharing of experiences and the pooling of resources. However, much of the spatial data collection efforts within Africa have been conducted in a decentralised and uncoordinated manner. Inter-sectoral collaboration initiatives should therefore be encouraged and receive funding priority. Africa could usefully build projects such as the Global Spatial Data Infrastructure (embedded within which is the SDI – Africa project) and the EIS – Africa  projects which aim to support ready access to geographic information to support decision making at all scales for multiple purposes. Geographic datasets are being developed for some countries in Africa through these initiatives, but a systematic programme is required to make geographic data readily available for the continent as a whole. A major programme (funded by an international body) is needed to take up this challenge. Priorities include, for example, the digitalisation of 1:250 000 and 1: 50 000 cartographic maps for countries that have them. Similarly, national geo-referenced health facility databases should be established. Inexpensive African data sets include the African data sampler (topographic, boundary and place data), long-term rainfall and temperature data  and raster population data . Development of such data sets are of paramount importance to ensure the growth of all sectors of GIS in Africa.
Widespread availability of small scale digital data (< 1: 50 000) for many countries within Africa is unlikely to ever become a reality. The most cost-effective answer to the data deficit and poor vital registration and health statistics problem in Africa is the establishment of sentinel geo-referenced demographic and health surveillance systems. This will enable the elucidation of small-scale disease patterns (e.g. diffusion dynamics) that could be modelled using coarser resolution data and the coverage extended. The INDEPTH network is a network of these sentinel surveillance sites, 23 of which are in Africa. The sites follow up a designated population intensively over time collecting highly accurate demographic, vital event (e.g. births, deaths, migrations) and health data on a routine basis. So far only a small proportion of the sites are fully geo-referenced but this is likely increase with the increase in GPS accuracy, falling prices and the obvious operational and research advantages of fully geo-referenced data. These sites can especially contribute (and have already contributed) to our understanding diseases with ill-defined relationships to the environment due to the detailed longitudinal collection of disease covariates. A recent spatial initiative in health is the West African Spatial Analysis Prototype (WASAP) that used geo-coded demographic and health survey (DHS) data to study the effects of climate on children's nutritional status, and the relationship between economic diversity and reproductive behaviour, as well as study the subnational geographic variation in health indicators at a regional level[87, 88]. Following the success of WASAP, more DHS sites have began to geo-code their survey data in an effort to facilitate cross-disciplinary analyses. The increasing availability of regional geo-referenced DHS data will facilitate a more comprehensive understanding of the patterns and processes of demographic and health changes and will lead to an increasing amount of GIS-based analyses of this important data in the near future.
In addition to the geo-coded household datasets outlined above, a large number of remotely sensed data sets, which have been already used extensively in health are available free of charge or at nominal cost. With the emergence of new technologies and techniques within remote sensing, there is likely to be a great improvement in the quality of such data sets and parallel improvement of GIS and related research products. Nevertheless, it is also true to say that so far, our ability to extract meaning and make useful decisions from remotely-sensed data has not kept pace with the developments in this field.
The issue of scale is one that is poorly understood in the disease arena. Disease patterns and processes evident at one scale are not necessarily evident at another. Moreover, correlations between explanatory variables and outcomes may even be (seemingly) reversed at different scales. This has led to a significant amount of confusion when hypotheses are rejected at one scale and not at another. Sometimes it is advisable to use coarser resolution data to mask out small scale heterogeneity. For example, the malaria modelling at a continental level used climatic data at a resolution of 0.05° [32, 34]. Higher resolution satellite data (sub kilometre) may obscure continental malaria patterns by exposing unnecessary small area variation. Ideally the resolution of the data should be driven by the application. However, given Africa's geographic data deficits, future research is needed to establish how applicable coarse resolution data sets are to modelling high resolution disease-specific dynamics and vice-versa. The above issues are as applicable to temporal resolution as they are to spatial resolution.
Another obstacle remaining to the growth of GIS in health in Africa is to convince role players (often from cash-strapped organisations) of the proven cost-effectiveness of GIS in the health arena. Even amongst the international scientific community, significant scepticism still exists surrounding the use of GIS technology in health. This problem will diminish in size as GIS continues to evolve. The parallel with epidemiology again warrants mentioning: In the same way that scepticism greeted epidemiologists who hypothesised that a relationship existed between smoking and lung cancer in the 1950s , so to will scepticism continue to plague GIS until it is firmly established as a science.
It is encouraging to note that several of the issues cited as obstacles to the growth of GIS in Africa a decade ago  have been overcome to some degree. These included the incompatibility of different software formats (data conversion problems), the non user-friendly interfaces of many systems and the lack of good inexpensive/free GIS software. Other obstacles such as the prohibitive costs of hardware have also become less of an issue. Perhaps a review in a decade's time will describe the increasing availability of inexpensive spatial data sets for Africa?
The 'mapping malaria risk in Africa' (MARA) research collaboration is an African research endeavour that makes extensive use of GIS technology. The collaboration has been highly successful in collating malaria data from around the continent, and producing a large number of scientific publications on a limited budget. The outputs of the research were then disseminated to countries throughout Africa in the form of digital (via the stand-alone MARA lite software) and hard copy maps. The collaboration overcame significant data deficits by creating its own base data sets and created a significant amount of GIS capacity in its five regional centres throughout the continent. During the setting up of the collaboration, significant scepticism was expressed by influential malaria scientists as to the ultimate value of a GIS approach, its logistical feasibility and cost-effectiveness. The collaboration is a testament to the fact that successful GIS initiatives can be undertaken in Africa.
Viable GIS health applications in Africa
The current software and hardware trends in combination with the realities faced in Africa have given rise to essentially, two broad categories of long-term feasible GIS health applications in Africa. The outputs of the categories will inform one another and are not mutually exclusive and may overlap. The first category involves the use of GIS as a research tool. These applications should seek to provide new insights into the spatial dimensions of disease and new methodologies to more cost-effectively allocate resources to health services. These types of applications will normally use high-end systems with significant analytical functionality and will usually involve a significant amount of additional data collection.
The second category of long-term viable GIS application concerns the use of GIS as a health planning and management tool and for exploratory data analysis. Generally speaking this kind of system will involve a low-end GIS. The primary goal of such a system will be to simply display and overlay basic health data concerning both health care facilities and disease patterns. These systems (normally vector-based) permit rapid manipulations of spatial data and display of the results so that the decision makers can use them for policy decisions. A further step could involve limited spatial queries and analysis such as buffering.
The outputs of the different categories of application will inform one another. As the data is geographically displayed using a management GIS and research questions are derived, collaborations can be initiated with institutions undertaking GIS research to test hypotheses and model disease distributions. Similarly, research GIS applications will inform GIS management applications to plan optimal resource allocation and intervention strategies, for example. The MARA collaboration is a successful example of this type of approach and is embedding several of its research outputs in the freely available GIS software HealthMapper (developed by WHO) for intervention planning in Africa at a district level.
A review of the health literature in Africa reveals the GIS bias towards so called 'environmental' diseases. In certain diseases, such as the vector-borne diseases (e.g. malaria, schistosomiasis, human helminth infections and trypanosomiasis) the environmental component in the determination of factors such as transmission intensity is extremely high. In other diseases, especially in the non-communicable category (e.g. multiple sclerosis) links to the environment are weak or non-existent. Some infectious diseases such as HIV and tuberculosis have moderately strong links to the environment. Thus there exists a continuum of diseases, on the one end there are those diseases in which GIS has limited research application and on the other there are those in which GIS is highly applicable. This continuum does not relate to the availability of ancillary data sets but rather to the inherent nature of the disease itself.
Not only does Africa have the highest burden of disease of all the continents, but it is the continent in which the greatest component of the burden is contributed by so called 'environmentally dependent' diseases. In addition, the phenomenon of climate change is likely to hit hardest in Africa  on account of its greater rainfall variability and the proportion of 'ecothermic infectious diseases'. This makes the potential applications of GIS in health particularly relevant to Africa, i.e. GIS in health has greater relevance and inherent potential in Africa than it does in the United States or Europe for example. Unfortunately, this reality is not reflected in the literature or in practice. Thus, we concur with authors [92, 93] who have concluded that GIS is an appropriate technology for developing countries (despite the fact that in some ways GIS appears to contradict the principles of appropriate technology because of its sometimes high cost and often high levels of expertise required) since many issues of poverty relate to large scale problems requiring integration of large spatial datasets. Furthermore, the success of participatory approaches for the transfer of GIS technology by the MARA project and in other developing country settings  could serve as a useful framework for future projects.
The ability to map spatial and temporal variation in disease risk is more important than ever given the ever-increasing disease burden in Africa. GIS allows the planning of control strategies and the delivering of interventions where the need is greatest, and sustainable success is most likely. Despite some obstacles, GIS holds considerable promise for health research and development in Africa. The global trend towards faster, more powerful computers, user-friendly software and falling prices combined with the magnitude and nature of Africa's disease burden and lack of reliable disease statistics makes it a viable, relevant and powerful technology for health research and management in Africa.
List of abbreviations
DHS – Demographic and Health Survey
DOT – Directly observed treatment
EIS – Environmental Information Systems
GPS – Global Positioning System
HIV – Human Immunodeficiency Virus
MARA – Mapping Malaria Risk in Africa
SDI – Spatial Development Initiative
WASAP – West African Spatial Analysis Prototype
WHO – World Health Organisation
David le Sueur died unexpectedly during the advanced stages of manuscript preparation. Frank Tanser wishes to acknowledge him for conceiving and driving the Mapping Malaria Risk in Africa (MARA) initiative and for his unsurpassed lifetime contribution to the field of malaria mapping and modelling and to GIS in health in general. This research was jointly funded by the South African Medical Research Council and the Wellcome Trust.
- Kalipeni E: Health and disease in southern Africa: a comparative and vulnerability perspective. Soc Sci Med. 2000, 50: 965-83. 10.1016/S0277-9536(99)00348-2.PubMedView ArticleGoogle Scholar
- Stock R: Africa South of the Sahara: a geographic interpretation. New York: Guilford Press. 1995Google Scholar
- Kloos H, Zein ZA: The ecology of health and disease in Ethiopia. Boulder, Colarado: Westveiw Press. 1993Google Scholar
- Gesler W: The uses of spatial analysis in medical geography: a review. Soc Sci Med. 1986, 23: 963-73. 10.1016/0277-9536(86)90253-4.PubMedView ArticleGoogle Scholar
- Mayer JD: The role of spatial analysis and geographic data in the detection of disease causation. Soc Sci Med. 1983, 17: 1213-21. 10.1016/0277-9536(83)90014-X.PubMedView ArticleGoogle Scholar
- Twigg L: Health based geographical information systems: their potential examined in the light of existing data sources. Soc Sci Med. 1990, 30: 143-55. 10.1016/0277-9536(90)90337-R.PubMedView ArticleGoogle Scholar
- Marshal R: A review of methods for the statistical analysis of spatial patterns of disease. J R Statist Soc A. 1991, 154: 421-441.View ArticleGoogle Scholar
- Scholten HJ, de Lepper MJ: The benefits of the application of geographical information systems in public and environmental health. World Health Stat Q. 1991, 44: 160-70.PubMedGoogle Scholar
- Walter SD: Visual and statistical assessment of spatial clustering in mapped data. Stat Med. 1993, 12: 1275-91.PubMedView ArticleGoogle Scholar
- Briggs DJ, Elliott P: The use of geographical information systems in studies on environment and health. World Health Stat Q. 1995, 48: 85-94.PubMedGoogle Scholar
- Clarke KC, McLafferty SL, Tempalski BJ: On epidemiology and geographic information systems: a review and discussion of future directions. Emerg Infect Dis. 1996, 2: 85-92.PubMedPubMed CentralView ArticleGoogle Scholar
- Vine M: Geographic information systems: their use in environmental epidemiological research. J Environ Health. 1998, 61: 7-10.Google Scholar
- Moore DA, Carpenter TE: Spatial analytical methods and geographic information systems: use in health research and epidemiology. Epidemiol Rev. 1999, 21: 143-61.PubMedView ArticleGoogle Scholar
- Loslier L: Geographical information systems (GIS) from a health perspective. In: GIS for health and the environment. Edited by: De Savigny D, Wijeyaratne P. 1994, Ottawa: IDRC, 13-20.Google Scholar
- World Bank: Overview of the World Bank's work in sub-Saharan Africa. Washington D.C.: World Bank. 2000Google Scholar
- Snow RW, Craig M, Deichmann U, Marsh K: Estimating mortality, morbidity and disability due to malaria among Africa's non-pregnant population. Bull World Health Organ. 1999, 77: 624-40.PubMedPubMed CentralGoogle Scholar
- Murray CJ, Lopez AD: Mortality by cause for eight regions of the world: Global Burden of Disease Study. Lancet. 1997, 349: 1269-76. 10.1016/S0140-6736(96)07493-4.PubMedView ArticleGoogle Scholar
- WHO: The World Health Report 2000. Health Systems: Improving performance. Geneva: World Health Organisation. 2000Google Scholar
- World Bank: World development report, 2000. Washington D.C.: World Bank. 2000Google Scholar
- UNAIDS: AIDS epidemic update: December 1998. Geneva: UNAIDS. 1998Google Scholar
- World Bank: World development report, 1993. Washington D.C.: World Bank. 1993Google Scholar
- WHO: TB – a global emergency. WHO report on the TB epidemic. Geneva: World Health Organisation. 1994Google Scholar
- Raviglione MC, Dye C, Schmidt S, Kochi A: Assessment of worldwide tuberculosis control. WHO Global Surveillance and Monitoring Project. Lancet. 1997, 350: 624-9. 10.1016/S0140-6736(97)04146-9.PubMedView ArticleGoogle Scholar
- WHO: Global tuberculosis control. Geneva: World Health Organisation. 1997Google Scholar
- WHO: The world health report 1996: fighting disease fostering development. Geneva: World Health Organisation. 1996Google Scholar
- Anderson J, Maclean M, Davies C: Malaria research. An audit of international activity. London: Wellcome Trust Publishing. 1996, 1Google Scholar
- Marsh K, Snow RW: Malaria transmission and morbidity. Parassitologia. 1999, 41: 241-6.PubMedGoogle Scholar
- Trape JF, Pison G, Preziosi MP, Enel C, Desgrees du Lou A, Delaunay V, Samb B, Lagarde E, Molez JF, Simondon F: Impact of chloroquine resistance on malaria mortality. C R Acad Sci III. 1998, 321: 689-97. 10.1016/S0764-4469(98)80009-7.PubMedView ArticleGoogle Scholar
- Krishna S: Science, medicine, and the future. Malaria. Bmj. 1997, 315: 730-2.PubMedPubMed CentralView ArticleGoogle Scholar
- Nchinda TC: Malaria: a reemerging disease in Africa. Emerg Infect Dis. 1998, 4: 398-403.PubMedPubMed CentralView ArticleGoogle Scholar
- Charlwood JD, Bryan JH: A mark-recapture experiment with the filariasis vector Anopheles punctulatus in Papua New Guinea. Ann Trop Med Parasitol. 1987, 81: 429-36.PubMedGoogle Scholar
- Craig MH, Snow RW, le Sueur D: A climate-based distribution model of malaria transmission in sub- Saharan Africa. Parasitol Today. 1999, 15: 105-11. 10.1016/S0169-4758(99)01396-4.PubMedView ArticleGoogle Scholar
- MARA: Towards an Atlas of malaria risk in Africa: First technical report of the MARA/ARMA collaboration. Durban. 1998Google Scholar
- Tanser FC, Sharp B, le Sueur D: Malaria seasonality and the potential impact of climate change in Africa. Submitted for publication. 2002Google Scholar
- Hay SI, Snow RW, Rogers DJ: Predicting malaria seasons in Kenya using multitemporal meteorological satellite sensor data. Trans R Soc Trop Med Hyg. 1998, 92: 12-20.PubMedView ArticleGoogle Scholar
- Kleinschmidt I, Bagayoko M, Clarke GP, Craig M, Le Sueur D: A spatial statistical approach to malaria mapping. Int J Epidemiol. 2000, 29: 355-61. 10.1093/ije/29.2.355.PubMedView ArticleGoogle Scholar
- Thomson MC, Connor SJ, Milligan PJ, Flasse SP: The ecology of malaria – as seen from Earth-observation satellites. Ann Trop Med Parasitol. 1996, 90: 243-64.PubMedGoogle Scholar
- Snow RW, Gouws E, Omumbo J, Rapuoda B, Craig MH, Tanser FC, le Sueur D, Ouma J: Models to predict the intensity of Plasmodium falciparum transmission: applications to the burden of disease in Kenya. Trans R Soc Trop Med Hyg. 1998, 92: 601-6.PubMedView ArticleGoogle Scholar
- Thomson MC, Connor SJ, D'Alessandro U, Rowlingson B, Diggle P, Cresswell M, Greenwood B: Predicting malaria infection in Gambian children from satellite data and bed net use surveys: the importance of spatial correlation in the interpretation of results. Am J Trop Med Hyg. 1999, 61: 2-8.PubMedGoogle Scholar
- Thomas CJ, Lindsay SW: Local-scale variation in malaria infection amongst rural Gambian children estimated by satellite remote sensing. Trans R Soc Trop Med Hyg. 2000, 94: 159-63.PubMedView ArticleGoogle Scholar
- Rogers DJ, Randolph SE, Snow RW, Hay SI: Satellite imagery in the study and forecast of malaria. Nature. 2002, 415: 710-5. 10.1038/415710a.PubMedPubMed CentralView ArticleGoogle Scholar
- Snow RW, Craig MH, Deichman U, Le Sueur D: A continental risk map for malaria mortality among African children. Parasitol Today. 1999, 15: 99-104. 10.1016/S0169-4758(99)01395-2.PubMedView ArticleGoogle Scholar
- Hay SI, Cox J, Rogers DJ, Randolph SE, Stern DI, Shanks GD, Myers MF, Snow RW: Climate change and the resurgence of malaria in the East African highlands. Nature. 2002, 415: 905-9. 10.1038/415905a.PubMedPubMed CentralView ArticleGoogle Scholar
- Lindsay SW, Martens WJ: Malaria in the African highlands: past, present and future. Bull World Health Organ. 1998, 76: 33-45.PubMedPubMed CentralGoogle Scholar
- Smith T, Charlwood JD, Takken W, Tanner M, Spiegelhalter DJ: Mapping the densities of malaria vectors within a single village. Acta Trop. 1995, 59: 1-18. 10.1016/0001-706X(94)00082-C.PubMedView ArticleGoogle Scholar
- Ribeiro JM, Seulu F, Abose T, Kidane G, Teklehaimanot A: Temporal and spatial distribution of anopheline mosquitos in an Ethiopian village: implications for malaria control strategies. Bull World Health Organ. 1996, 74: 299-305.PubMedPubMed CentralGoogle Scholar
- Coetzee M, Craig M, le Sueur D: Distribution of African malaria mosquitoes belonging to the Anopheles gambiae complex. Parasitol Today. 2000, 16: 74-7. 10.1016/S0169-4758(99)01563-X.PubMedView ArticleGoogle Scholar
- Minakawa N, Mutero CM, Githure JI, Beier JC, Yan G: Spatial distribution and habitat characterization of anopheline mosquito larvae in Western Kenya. Am J Trop Med Hyg. 1999, 61: 1010-6.PubMedGoogle Scholar
- Omumbo J, Ouma J, Rapuoda B, Craig MH, le Sueur D, Snow RW: Mapping malaria transmission intensity using geographical information systems (GIS): an example from Kenya. Ann Trop Med Parasitol. 1998, 92: 7-21. 10.1080/00034989860120.PubMedView ArticleGoogle Scholar
- Hightower AW, Ombok M, Otieno R, Odhiambo R, Oloo AJ, Lal AA, Nahlen BL, Hawley WA: A geographic information system applied to a malaria field study in western Kenya. Am J Trop Med Hyg. 1998, 58: 266-72.PubMedGoogle Scholar
- Martin C, Curtis B, Fraser C, Sharp B: The use of a GIS-based malaria information system for malaria research and control in South Africa. Health Place. 2002, 8: 227-36. 10.1016/S1353-8292(02)00008-4.PubMedView ArticleGoogle Scholar
- Booman M, Durrheim DN, La Grange K, Martin C, Mabuza AM, Zitha A, Mbokazi FM, Fraser C, Sharp BL: Using a geographical information system to plan a malaria control programme in South Africa. Bull World Health Organ. 2000, 78: 1438-44.PubMedPubMed CentralGoogle Scholar
- Schellenberg JA, Newell JN, Snow RW, Mung'ala V, Marsh K, Smith PG, Hayes RJ: An analysis of the geographical distribution of severe malaria in children in Kilifi District, Kenya. Int J Epidemiol. 1998, 27: 323-9. 10.1093/ije/27.2.323.PubMedView ArticleGoogle Scholar
- Beyers N, Gie RP, Zietsman HL, Kunneke M, Hauman J, Tatley M, Donald PR: The use of a geographical information system (GIS) to evaluate the distribution of tuberculosis in a high-incidence community. S Afr Med J. 1996, 86: 40-1.PubMedGoogle Scholar
- van Rie A, Beyers N, Gie RP, Kunneke M, Zietsman L, Donald PR: Childhood tuberculosis in an urban population in South Africa: burden and risk factor. Arch Dis Child. 1999, 80: 433-7.PubMedPubMed CentralView ArticleGoogle Scholar
- Tanser FC, Wilkinson D: Spatial implications of the tuberculosis DOTS strategy in rural South Africa: a novel application of geographical information system and global positioning system technologies. Trop Med Int Health. 1999, 4: 634-8. 10.1046/j.1365-3156.1999.00469.x.PubMedView ArticleGoogle Scholar
- Wilkinson D, Tanser FC: GIS/GPS to document increased access to community-based treatment for tuberculosis in Africa. Lancet. 1999, 354: 394-5. 10.1016/S0140-6736(99)01893-0.PubMedView ArticleGoogle Scholar
- Wilkinson D, Pillay M, Crump J, Lombard C, Davies GR, Sturm AW: Molecular epidemiology and transmission dynamics of Mycobacterium tuberculosis in rural Africa. Trop Med Int Health. 1997, 2: 747-53. 10.1046/j.1365-3156.1997.d01-386.x.PubMedView ArticleGoogle Scholar
- Amat-Roze JM: Geographic inequalities in HIV infection and AIDS in sub-Saharan Africa. Soc Sci Med. 1993, 36: 1247-56. 10.1016/0277-9536(93)90214-O.PubMedView ArticleGoogle Scholar
- Remy G: Epidemiologic distribution of HIV2 human immunodeficiency virus infection in sub-Saharan Africa. Med Trop (Mars). 1993, 53: 511-6.Google Scholar
- Remy G: Geographic distribution of HIV-1 infection in Central Africa: remarkable discontinuities. Ann Soc Belg Med Trop. 1993, 73: 127-42.PubMedGoogle Scholar
- Sokal DC, Buzingo T, Nitunga N, Kadende P, Standaert B: Geographic and temporal stability of HIV seroprevalence among pregnant women in Bujumbura, Burundi. Aids. 1993, 7: 1481-4.PubMedView ArticleGoogle Scholar
- Killewo J, Dahlgren L, Sandstrom A: Socio-geographical patterns of HIV-1 transmission in Kagera Region, Tanzania. Soc Sci Med. 1994, 38: 129-34. 10.1016/0277-9536(94)90307-7.PubMedView ArticleGoogle Scholar
- Weir SS, Morroni C, Coetzee N, Spencer J, Boerma JT: A pilot study of a rapid assessment method to identify places for AIDS prevention in Cape Town, South Africa. Sex Transm Infect. 2002, 78 (Suppl 1): i106-13.PubMedPubMed CentralView ArticleGoogle Scholar
- Tanser FC, Le Sueur D, Solarsh G, Wilkinson D: HIV heterogeneity and proximity of homestead to roads in rural South Africa: an exploration using a geographical information system. Trop Med Int Health. 2000, 5: 40-46. 10.1046/j.1365-3156.2000.00513.x.PubMedView ArticleGoogle Scholar
- Gould P: The slow plague: a geography of the AIDS pandemic. Cambridge, Massachusetts: Blackwell. 1993Google Scholar
- Low-Beer D, Stoneburner RL, Mukulu A: Empirical evidence for the severe but localized impact of AIDS on population structure. Nat Med. 1997, 3: 553-7.PubMedView ArticleGoogle Scholar
- Pickering H, Okongo M, Bwanika K, Nnalusiba B, Whitworth J: Sexual mixing patterns in Uganda: small-time urban/rural traders. Aids. 1996, 10: 533-6.PubMedView ArticleGoogle Scholar
- Golub A, Gorr WL, gould PR: Spatial diffusion of the HIV/AIDS epidemic: modelling implications and case study of the AIDS incidence in Ohio. Geogr Anal. 1993, 25: 85-100.View ArticleGoogle Scholar
- Loyotonnen M: The spatial diffusion of the human immunodeficiency virus type 1 in Finland, 1982–1987. Ann Assoc Am Geogr. 1991, 81: 127-51.View ArticleGoogle Scholar
- Kearns RA: AIDS and medical geography: embracing the other?. Progr hum geogr. 1996, 20: 123-131.View ArticleGoogle Scholar
- Zwarenstein M, Krige D, Wolff B: The use of a geographical information system for hospital catchment area research in Natal/KwaZulu. S Afr Med J. 1991, 80: 497-500.PubMedGoogle Scholar
- Tanser FC: The application of GIS technology to equitably distribute fieldworker workload in a large, rural South African health survey. Trop Med Int Health. 2002, 7: 80-90. 10.1046/j.1365-3156.2002.00825.x.PubMedView ArticleGoogle Scholar
- Tanser F, Hosegood V, Benzler J, Solarsh G: New approaches to spatially analyse primary health care usage patterns in rural South Africa. Trop Med Int Health. 2001, 6: 826-38. 10.1046/j.1365-3156.2001.00794.x.PubMedView ArticleGoogle Scholar
- Perry B, Gesler W: Physical access to primary health care in Andean Bolivia. Soc Sci Med. 2000, 50: 1177-88. 10.1016/S0277-9536(99)00364-0.PubMedView ArticleGoogle Scholar
- Goodchild M: Geographical information science. Int J GIS. 1992, 6: 31-45.Google Scholar
- Rothman KJ: Modern Epidemiology. Boston/Toronto: Little, Brown and Company. 1986Google Scholar
- Taylor DRF: GIS and developing nations. In: Geographical information systems. Edited by: London: Longman. 1991, Maguire D, Goodchild M, Rhind D, 2: 71-84.Google Scholar
- Nijkamp P, De Jong W: Training needs in information systems for local and regional development. Regional Development Dialogue. 1987, 8: 72-119.Google Scholar
- Holland P, Reichardt ME, Nebert D, Blake S, Robertson D: The global spatial data infrastructure initiative and its relationship to the vision of a digital earth. In: International Symposium on Digital Earth; Beijing, China. 1999Google Scholar
- EIS-Africa: Geo-information supports decision-making in Africa. Pretoria: EIS-Africa. 2002Google Scholar
- WRI: Africa Data Sampler. CD-ROM edition 1. Washington D.C.: World Resources Institute. 1995Google Scholar
- Hutchinson MF, Nix HA, McMahan JP, Ord KD: Africa – A topographic and climatic database, CD-ROM (1):. Centre for Resource and Environmental Studies, Australian National University. 1995Google Scholar
- Deichmann U: Africa population database:. National Centre for Geographic Information and Analysis and United Nations Environment Programme, World Resources Institute. 1996,http://grid2.cr.usgs.gov/globalpop/africaGoogle Scholar
- Guest R: Health care in poor countries: For 80 cents more. The Economist. 2002, August 15thGoogle Scholar
- INDEPTH: Population, Health and Survival at INDEPTH Sites. In: Population and Health in Developing Countries, vol. 1. Ottawa Canada: IDRC. 2002, 356pp-Google Scholar
- Hill ND: Creating social borders from the WASAP data sets. Calverton, Maryland: Macro International. 1998Google Scholar
- Rutstein SO: Cluster typing procedures. Calverton, Maryland: Macro International. 2000Google Scholar
- Hay SI, Randolph SE, Rogers DJ: Remote sensing and geographical information systems in epidemiology. London: Academic Press. 2000Google Scholar
- Korte G: Weighing GIS benefits with financial analysis. Government Finance Review. 1996, 12: 48-52.Google Scholar
- Hastings D, Clarke D: GIS in Africa: problems, challenges and opportunities for co-operation. IJGIS. 1991, 5: 29-39.Google Scholar
- Yapa L: Is GIS appropriate technology?. IJGIS. 1991, 5: 41-58.Google Scholar
- Dunn C, Atkins P, Townsend J: GIS for development: a contradiction in terms?. Area. 1997, 29: 151-159.View ArticleGoogle Scholar
- Hutchinson CF, Todedano J: Guidelines for demonstrating geographical information systems based on participatory development. IJGIS. 1993, 7: 453-461.Google Scholar
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