Low-income countries would benefit significantly from the expanded use of Geographic Information Systems (GIS) in the analysis of disease distribution, since GIS could provide more accurate information about disease incidence and prevalence rates and would allow for better allocation of the limited resources available for public health [1–5]. However, these areas face significant barriers to the implementation of GIS for spatial epidemiology, due both to a lack of disease data (because of limitations in disease detection and reporting systems) and to the non-existence of detailed maps, especially in areas affected by conflict, population displacement, and rapid urbanization. Without accurate population, disease, and spatial data, it will not be possible to implement effective surveillance systems in these countries. Increasing the accuracy of these measures will require an improvement in data collection and management systems as well as a significant increase in the ability of scientists in low-income countries to apply epidemiological, laboratory, and GIS techniques to local health concerns [6–8].
Freely-available mapping and analysis tools and free or low-cost data and image sources (such as Google Earth) can be a greatly beneficial starting point for generating basic map features in areas where this information is not already available. However, effective disease surveillance requires that physical geographic information be supplemented by data about social and population factors that can be mapped at a fine scale. Combining information about distances from water, waste disposal areas, swampy areas, and other physical characteristics as well as information on population density, economic factors, and the location of health resources provides a more complete picture of health and disease by providing information about potential mitigating and debilitating factors. Even simple information about the location of patients' homes and the characteristics of those dwellings can provide helpful information about both physical and socioeconomic parameters that can be incorporated into a health GIS, information that is rarely available in low-income countries with limited access to technology. As the amount of remote imagery available for use in lower-income areas is expanding, so is the need for ground-truth validation of these high-resolution maps, which must be completed before advanced GIS analysis can be conducted [9–14].
Methods that engage and empower communities and allow local residents to incorporate their knowledge into the production of a GIS are a promising emerging approach for assembling ground truth data. Public Participation Geographic Information Systems (PPGIS) emerged in the 1980s as a formal method to involve the "public" in the creation and use of geographic information [15–17]. PPGIS and related approaches, such as participatory GIS (PGIS), community integrated GIS, and GIS for participation, have been adapted for use in a variety of settings with various levels of local participation, different types of communities, and a range of intended applications [17–20]. In each of these models, the goal is to integrate local knowledge with "expert" data and techniques.
In this study, we developed and used participatory methods to provide a low-cost solution to address mapping issues in Bo, Sierra Leone, and to begin populating a GIS with population statistics that can be used to address many community issues. Our specific aims were as follows: (1) to create a map of the sections (neighborhoods) of Bo by employing a participatory mapping method, which included interviews of knowledgeable long-term local residents and consultation with municipal authorities in the Bo City Council and local officials and (2) to estimate total population in a few sections by combining data from the map, the 2004 census, and population data obtained from household surveys. As part of the first aim and an example of how maps can be applied to health-related research, an analysis of visits to the Mercy Hospital Laboratory was performed using hospital records. Future studies will use the information from the map and surveys of households sampled from residential buildings identified on the map to assist with the initiation of an active infectious disease surveillance system and the analysis of the social and environmental factors contributing to the incidence and prevalence of diseases.