Leveraging community health worker system to map a mountainous rural district in low resource setting: a low-cost approach to expand use of geographic information systems for public health

Abstract

BackgroundGeographic Information Systems (GIS) have become an important tool in monitoring and improving health services, particularly at local levels. However, GIS data are often unavailable in rural settings and village-level mapping is resource-intensive. This study describes the use of community health workers’ (CHW) supervisors to map villages in a mountainous rural district of Northern Rwanda and subsequent use of these data to map village-level variability in safe water availability.MethodsWe developed a low literacy and skills-focused training in the local language (Kinyarwanda) to train 86 CHW Supervisors and 25 nurses in charge of community health at the health center (HC) and health post (HP) levels to collect the geographic coordinates of the villages using Global Positioning Systems (GPS). Data were validated through meetings with key stakeholders at the sub-district and district levels and joined using ArcMap 10 Geo-processing tools. Costs were calculated using program budgets and activities’ records, and compared with the estimated costs of mapping using a separate, trained GIS team. To demonstrate the usefulness of this work, we mapped drinking water sources (DWS) from data collected by CHW supervisors from the chief of the village. DWSs were categorized as safe versus unsafe using World Health Organization definitions.ResultFollowing training, each CHW Supervisor spent five days collecting data on the villages in their coverage area. Over 12 months, the CHW supervisors mapped the district’s 573 villages using 12 shared GPS devices. Sector maps were produced and distributed to local officials. The cost of mapping using CHW supervisors was $29,692, about two times less than the estimated cost of mapping using a trained and dedicated GIS team ($60,112). The availability of local mapping was able to rapidly identify village-level disparities in DWS, with lower access in populations living near to lakes and wetlands (p < .001).ConclusionExisting national CHW system can be leveraged to inexpensively and rapidly map villages even in mountainous rural areas. These data are important to provide managers and decision makers with local-level GIS data to rapidly identify variability in health and other related services to better target and evaluate interventions.

Using a geographic information system to enhance patient access to point-of-care diagnostics in a limited-resource setting

“…If population data cannot be obtained through outside sources, then it must be collected. While this can be a time and resource intensive process whose cost may not justify the benefits, a recent study showed that low cost collections of village locations can be done easily and rapidly [ 18 ].…”

Evolution of research in health geographics through the International Journal of Health Geographics (2002–2015)

“…What are these articles, and in what way do they differ from those previously selected? Some are centered on data-related methodological difficulties, whether these are linked to geo-referencing or cartographic representation, for example they are about how difficult it is to have access to Gis data on a fine scale in some rural regions, and how useful it is then to rely on previously trained community health workers in order to obtain data on the repartition of safe water for people in northern Rwanda [ 37 ]. These articles also deal with the development of pertinent cartographic approaches to represent, for example, the risk linked to natural-focal diseases [ 38 ], or for better distributing healthcare resources in developing countries [ 39 ].…”

Decision-Making Framework for Validation of Data Collection Process in a Survey with GPS Data

“…Characteristics of several methods can be seen in the table 9. [11], [15], [16], [17] [10], [11], [12], [13], [14], [15], [16] [15], [16], [17], [18], [19] Accuracy After the method was chosen, the rest of our research will continue with previous research, find something that is a contribution from research comparison of several studies with research on the validation of the enumeration process in a survey can be seen in table 10. Table 10 shows comparison of several study in decision-making with GPS data to analyze human behavior.…”