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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

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Geographic 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.
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  METHODOLOGY Open Access Leveraging community health worker system tomap a mountainous rural district in low resourcesetting: a low-cost approach to expand use of geographic information systems for public health Fabien Munyaneza 1,2* , Lisa R Hirschhorn 3,4,5 , Cheryl L Amoroso 2 , Laetitia Nyirazinyoye 1 , Ermyas Birru 3 ,Jean Claude Mugunga 3 , Rachel M Murekatete 6,7 and Joseph Ntaganira 1 Abstract Background:  Geographic Information Systems (GIS) have become an important tool in monitoring and improvinghealth services, particularly at local levels. However, GIS data are often unavailable in rural settings and village-levelmapping is resource-intensive. This study describes the use of community health workers ’  (CHW) supervisors tomap villages in a mountainous rural district of Northern Rwanda and subsequent use of these data to mapvillage-level variability in safe water availability. Methods:  We developed a low literacy and skills-focused training in the local language (Kinyarwanda) to train 86CHW Supervisors and 25 nurses in charge of community health at the health center (HC) and health post (HP) levelsto collect the geographic coordinates of the villages using Global Positioning Systems (GPS). Data were validatedthrough meetings with key stakeholders at the sub-district and district levels and joined using ArcMap 10Geo-processing tools. Costs were calculated using program budgets and activities ’  records, and compared with theestimated costs of mapping using a separate, trained GIS team. To demonstrate the usefulness of this work, wemapped 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. Result:  Following training, each CHW Supervisor spent five days collecting data on the villages in their coveragearea. Over 12 months, the CHW supervisors mapped the district ’ s 573 villages using 12 shared GPS devices. Sectormaps 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). Theavailability of local mapping was able to rapidly identify village-level disparities in DWS, with lower access inpopulations living near to lakes and wetlands (p < .001). Conclusion:  Existing national CHW system can be leveraged to inexpensively and rapidly map villages even inmountainous rural areas. These data are important to provide managers and decision makers with local-level GISdata to rapidly identify variability in health and other related services to better target and evaluate interventions. Keywords:  GPS, Community health workers, GIS, Costing, Disparities, Resource limited settings, Drinking water sources * Correspondence: fabienmuny@gmail.com 1 College of Medicine and Health Sciences, University of Rwanda, Kigali,Rwanda 2 Partners In Health/Inshuti Mu Buzima, Kigali, RwandaFull list of author information is available at the end of the article INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS © 2014 Munyaneza et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the CreativeCommons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the srcinal work is properly credited. The Creative Commons Public DomainDedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article,unless otherwise stated. Munyaneza  et al. International Journal of Health Geographics  2014,  13 :49http://www.ij-healthgeographics.com/content/13/1/49  Background The spread of Geographic Information Systems (GIS)  – a set of tools to capture, store, transform, analyse, anddisplay spatial data  – has improved spatial analysis of health related services and population health [1-4]. Healthdata combined with geographic information allows us toanalyse the spatial variation of diseases burden, mortality,morbidity, physical access to health care and social orenvironmental determinants of health outcomes [1,2,5-9].The transformation of detailed data into maps can facili-tate communication of geographic distribution of healthchallenges in different communities and identify areas forintervention [4,5,10].The use of GIS in low resource settings has been ham-pered by a number of factors including data availability,software, expertise, and economic resources needed[4,6,11]. Participatory mapping is a map productionprocess by local communities with the support of gov-ernmental institution, non-governmental organizations(NGOs) or academic institutions [12]. The UnitedNations (UN) Millennium Development Goals (MDGs)and the World Summit on the Information Society sug-gested utilization of a participatory approach to promoteequal access to information and knowledge sharing [13].This approach could offer a relatively low resource me-thod to map communities in rural settings.Partners In Health/Inshuti Mu Buzima (PIH/IMB) haspartnered with the Rwandan Ministry of Health (MoH)since 2005 to provide support to three districts (Kayonza,Kirehe and Burera) in rural Rwanda, including supportingthe community health workers (CHW) system to providecommunity-based care. Since 2009, PIH/IMB has usedGIS to supplement existing monitoring and evaluationefforts in supported areas to better target district-widehealth system strengthening interventions [14]. Villagelocation mapping was done using a trained GIS team of PIH/IMB in Kayonza (Rwinkwavu District Hospital catch-ment area only) and Kirehe districts and was relatively resource intensive. In the third district, Burera, a moun-tainous rural district with poor road access, we adapted acommunity participatory approach to conduct village-level mapping. We describe this approach of leveragingRwanda ’ s national CHW program to map village locations,and demonstrate the utility of this process through identi-fication of geographic disparities in critical servicesthrough mapping of village-level access to safe water. Methods Study area: Burera district, one of the 30 districts of Rwanda, is located in the Northern Province, neighbor-ing Uganda. Burera is a mountainous rural district witha topography ranging between 1728 m to 4098 m of alti-tude above sea level with an area of 646 km 2 (Figure 1).The district is subdivided by Rwandan administrativeboundaries into 17 sectors, with 69 sub-sector divisions(cells) and 573 villages, with each cell containing be-tween five and 16 villages (Figure 1).Rwanda National CHW network: Each village has fourCHWs in charge of community health and each cell hasa CHW supervisor. CHW supervisors live in the com-munity, and have completed a minimum of primary school education. Each health center (HC) or health post(HP) is staffed with a nurse who supervises the commu-nity health activities in the catchment area.Community mapping: Four meetings were held forauthorities from the district, district hospital (DH), HCsand some CHW supervisors in order to encourage par-ticipation and ownership in the mapping process. Duringthese meetings we reviewed the approach and goals of GIS mapping and potential to help efforts to improvehealth care in Burera district.Training process: We developed a three day trainingprogram and training manual in English and Kinyarwandato build knowledge around the use of Global PositioningSystem (GPS) device and the skills needed to collect thedata. The first day of the training included the introduc-tion and explanation of the purpose of the activity, the value of map analysis, and an introduction to GPS func-tionalities. The second day focused on the use of the GPSdevice; emphasizing taking GPS coordinates points. Thelast day was field-based practice, where trainees collectedGPS coordinates that were then validated, and also dis-cussed challenges and solutions. CHW supervisors whohad more difficulty using GPS were identified by trainersand given coaching on their first day of data collection toensure data quality. In total 111 people were trained, 69cell-level CHW supervisors, 17 sector-level CHW supervi-sors, and 25 nurses in charge of community health at HCsand HPs. A full-time Burera-based District GIS projectassistant was hired by PIH/IMB who provided to training, validation and support for the CHW supervisors fieldwork and data entry.CHW staff: All CHW supervisors and nurses in chargeof community health at HC and HP level were includedin the training. Different roles were attributed to eachcadre: cell-level CHW supervisors collected the villageGPS coordinates; sector-level CHW supervisors and com-munity health nurses provided supportive supervision andorganization of the field data collection along with theGIS project assistant.GPS data collection: Cell-level CHW supervisors col-lected GPS coordinates of all villages in their cell overfive days. The GPS device was used to collect longitudeand latitude information of a specific location, and thecollected data was stored as point features in the device.Every data collector had a GPS with two extra fully charged batteries. We started data collection in the cen-tral part of the district, and then continued north to the Munyaneza  et al. International Journal of Health Geographics  2014,  13 :49 Page 2 of 8http://www.ij-healthgeographics.com/content/13/1/49  mountainous part of the district in the dry season (July,September, and October) due to transportation-relatedchallenges presented by the rainy season. Village loca-tion was mapped based on where population gatheredfor meeting places like village office or village chief  ’ shouseDrinking Water Sources (DWS) and population data:Information on DWS and population was provided by the elected chief of the village in 2013 through a brief survey administered by the CHW supervisor. DWSswere classified as safe water (water from ImprovedDrinking Water Source (IDWS)) and unsafe water (waterfrom Unimproved Drinking Water Source (UDWS)) usingthe World Health Organization (WHO) definitions [15].IDWSs were defined as adequately protected from outsidecontaminations, including piped household water con-nection, public standpipe borehole, protected dug well,protected spring and rainwater collection. UDWSs werethose inadequately protected from outside contamination,including unprotected dug well and spring, surface water, vendor-provided water (cart with small tank/drum, tankertruck), and bottled water (bottled water is consideredimproved only when the household use another improvedsource for cooking and personal hygiene) [15]. Since theremay be multiple drinking water sources in a village, weclassified the most frequently used drinking water sourceas primary and others were classified as secondary.Other data: The list of villages ’  names and their corre-sponding cells and sectors of 2009 was obtained fromthe Ministry of Local Government (MINALOC). Rwandancountry, district, sector boundary, road network shapefilesreleased in 2006 and lakes, wetlands released in 1992 werecollected from University of Rwanda Center of Geogra-phic Information System and Remote Sensing (UR-CGIS).Cell boundary shapefile released in 2008 was obtainedfrom National Institute of Statistics of Rwanda (NISR).Village GPS coordinates were imported as a shapefileusing DNR Garmin software (version 5.03.0002). Coordi-nates were joined to the village ’ s name, to allow the visualization in the map. Maps were produced (Figures 1,2 and 3) using geo-processing tool in ArcGIS 10.1.To map village DWS, the shapefile was joined with Figure 1  Location of the study area as well as its topography, sectors, cells, and villages. A : location of Rwanda in Africa,  B : location of Burera district as one of 30 district of Rwanda,  C : Burera district subdivisions; 17 sectors, 69 cells and 573 villages, and the Digital Elevation Modal(DEM) showing elevation and terrain of Burera district. Munyaneza  et al. International Journal of Health Geographics  2014,  13 :49 Page 3 of 8http://www.ij-healthgeographics.com/content/13/1/49  information on the DWS and population data andcombined with district lakes, and wetlands.Map validation and distribution: CHW supervisor-collected GPS coordinates were first validated by theGIS assistant. He randomly recollected at least two vil-lages GPS coordinates for each cell, and compared themto those collected by the CHW supervisor. Secondly, wemet with local authorities from cell, sector, district, HCand DH to validate villages GPS locations mapped.Seventeen sector-level validation meetings were heldwith 124 authorities ’  participants. The district map wasthen validated by district-level authorities during a valid-ation meeting, including 27 participants (vice mayorsand district office authorities in charge of health, landand environment, district hospital administrators andother district officials). Once validated, the maps wereprinted out, laminated and distributed to the district,sector, cell and health facilities for posting and adminis-trative use.Cost: Costs of the mapping process were estimatedfrom a health system perspective, using the data fromprogram budgets and financial activity records. Beforestarting the mapping in Burera district, the GIS team atPIH/IMB had just concluded a similar mapping exercisein Kirehe district using a trained full-time PIH/IMB GISteam. We compared the costs of mapping by the CHW supervisors in Burera, with modelled estimates thatwould have been incurred if staffing resources similar tothose of the Kirehe mapping were utilized. To get thecost of personnel and equipment (transport, computersand other devices), we first estimated their capacity ratesin hours available for mapping activities during the entiremapping period (weekday hours minus holidays, time-offsand weekends). The concept of useful-life was used to es-timate the depreciation and present value of equipmentsuch as vehicles, computers, and GPSs that last for morethan a year. Indirect and overhead costs such as PIH/IMB ’ s organizational and administrative spending relatedto this mapping were very minimal in both mappingmethods, and were therefore ignored. The main cost cat-egories were training, data collection and mapping, valid-ation, and dissemination (Table 1). Unit and total costs Figure 2  Villages mapped by quarter. A : 125 villages (three sectors) in quarter two of 2011 (April  –  June).  B : 220 villages (six sectors) in quarterthree of 2011 (July  –  September).  C : 116 villages (four sectors) in quarter four 2011 (October  –  December).  D : 112 villages (four sectors) in quarterone of 2012 (January - March).  E : Total of 573 villages of 17 sectors in four quarters. Munyaneza  et al. International Journal of Health Geographics  2014,  13 :49 Page 4 of 8http://www.ij-healthgeographics.com/content/13/1/49  were calculated in Rwandan Francs (RWF) and convertedinto USD using the median exchange rates of April 2011to March 2012, actual spending period.Mapping village level DWS: DWS was overlaid ontothe maps to create a village-level district-wide map of access to safe water. We categorized villages as proximalto lakes and wetlands if they were located within onekilometer Euclidean distance and then used chi squaredtest for association between lake and wetland proximity and DWS type. Figure 3  Drinking water distributions by village and sector. A : The dot represented the location of villages while the size of dot was proportionto the number of population in the village. The dots in green color represented safe water while dots in red color represented unsafe water. Area inyellow was a one kilometer Euclidian distance from lake in blue and wetland in green and while lines.  B : Represented the percentage of populationusing safe drinking water by sector which decreased from green to yellow and red, from 100% (the highest) to 32% (the lowest). Table 1 Cost of the intervention using CHW supervisors compared to GIS team of PIH/IMB Cost category Items counted in costing Mapping byCHW supervisorsMapping by GISteam of PIH/IMBUSD % USD %  Trainings and orientation Training manual, room, training materials, meals,refreshment, transport and certificates4,335 15% 72 0%Data collection and mapping Salary GIS coordinator and assistant, hiring GIS assistant,salary CHW supervisors/5 days, transport (vehicle, fuel, and driver),motorcycle rental, meals, communication, accommodation,software (one year license of Arc GIS), laptops, rain coats, GPS devices*20,166 68% 54,988 91%Validation (sector & district levels) Transport, per diem, meals, refreshment, printing 3,345 11% 3,397 6%Dissemination Printing, lamination, transport and per diem 1,846 6% 1,655 3% Total 29,692 100 %  60,112 100 % *GPS devices which were shared by the mappers. Munyaneza  et al. International Journal of Health Geographics  2014,  13 :49 Page 5 of 8http://www.ij-healthgeographics.com/content/13/1/49
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